A Smart and Minimum-intrusive Monitoring Framework Design for
Health Assessment of the Elderly
A dissertation submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in the Department of Mechanical Engineering of the College of Engineering and Applied Science
By Chuan Jiang M.S. University of Cincinnati (2012)
Doctoral Committee: Dr. Samuel Huang (Chair) Dr. Jay Kim Dr. David Thompson Dr. Gregg Warshaw Dr. Evelyn Fitzwater
April 2015
Abstract
The continuously increasing trend of world aging has spurred the development of healthcare services and facilities for the elderly population. A variety of daily activity and behavior monitoring systems are built to promote independent living wellbeing and provide tremendous cost benefits for the elderly. However, it has been observed that none of these efforts uses a well-defined behavioral model as theoretical basis for healthcare monitoring of the elderly. The existing behavioral models haven’t been demonstrated to be highly related to the health status of the elderly. These problems obstructed a lot for achieving convincible results in the healthcare monitoring applications.
To deal with this issue, the proposed research designs a systematic framework of an intelligent, minimum-intrusive, and low-cost sensing system for monitoring behaviors of the elderly and measuring or assessing their health status. A multi-dimensional behavioral model is developed based on instruments used in functional healthcare of the elderly. Based on this behavioral model, a high-level conceptual schema is illustrated to show how sensors are mapped to behavioral items that can be measured using sensors. A practical data acquisition strategy is established and sensors are selected to collect data for room occupancy, appliance usage and activity tracking. The collected data are analyzed and some features have been extracted to characterize the daily behavioral patterns of the monitored subject. A preliminary work plan for behavioral deviation detection and psychometric property testing is proposed. Also, methods and tools are selected to visualize and integrate the health index of each dimension of behavioral model of the elderly.
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This research provides a guideline for developing data-driven based health assessment instrumentation in healthcare monitoring applications for the elderly. Ultimately, the proposed system would be useful to improve elders’ quality of life, lower their healthcare cost, and reduce the reliance on caregivers.
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Acknowledgements
I would like to express my sincere respect and gratitude to my advisor, Dr. Samuel Huang, for his visionary guidance and persistent support through my doctoral study at the
University of Cincinnati. The help and encouragement of Dr. Huang inspired me a lot to gain experience in research and made me grow up as a more mature person in life. I am also grateful to Dr. Jay Kim, Dr. David Thompson, Dr. Gregg Warshaw and Dr. Evelyn
Fitzwater for serving on my doctoral committee. I am also thankful to Dr. Nuo Xu, Dr.
Dengyao Mo, Dr. Rongsheng Gong, Dr. Hua Li, Mr. Yi Zhang, Mr. Weizeng Ni, Mr. Kailun
Yuan, Mr. Suryanarayana Yaddanapudi, Mr. Vishnu Madhvarayan and many others for their help during my doctoral study. Lastly, I would like to express sincere appreciation to my parents for their constant consideration and support over the years.
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Table of Contents
Abstract……………………………………………………………………………………………ii Acknowledgements………………………………….…………………………………………….v Table of Contents….……………………………………………………………………………vi List of Figures…….……………………………………………….……………………………viii List of Tables…………………………………………………….…………………..……………x 1. Introduction…………………..…………………………………………………………………1 1.1. Introduction…………………..……………………………………..………………………1 1.2. Motivation…………………..……………………………………..………………..………2 1.3. Research Objectives………..……………………………………..………………..….……3 1.4. Dissertation Organization…..……………………………………..………………..….……5 2. Issues and State-of-art on Healthcare Monitoring for the Elderly…..…………………….……6 2.1. Patent Search on Healthcare Monitoring Systems of the Elderly…..………………....……6 2.2. Literature Review on Healthcare Monitoring of the Elderly…..………………..…....……11 2.2.1. Sensors………..……………………………………..………………………...….……12 2.2.2. Behavior Modeling………………………………..………………………...…....……14 2.2.3. Behavior Data Analysis………………………………..………….………...…....……18 2.3. Summary………………………………..………….………...…....…………………….…21 3. Development of Behavioral Model of the Elderly and Integrated Design of the Smart and Minimum-intrusive Monitoring System……..………….………...…....………………….…26 3.1. Concepts and Issues in Health Measurement of the Elderly…...…....………………….…26 3.1.1. Health Domains of Elderly and Commonly Used Assessment Scales…...…....………26 3.1.2. Psychometric Properties of Health Measurement Scales…………….…...…....………30 3.1.2.1. Reliability……..………….………...…....……………………………...……….…30 3.1.2.2. Validity……..………….………...…....……………………………...………….…31 3.1.2.3. Responsiveness……….………...…....……………………………...……..…….…34 3.2. Measurement Scales Used in Functional Healthcare and Behavioral Model of the Elderly……….………...…....………………………………………...………...……..…….…35 3.2.1. Measurement Scales Used in Functional Healthcare of the Elderly…...……..……..…35 3.2.2. Behavioral Model of the Elderly……………………………...………...……..…….…38
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3.3. Smart Apartment Overview and Sensor Selection for the Smart and Minimum-intrusive Monitoring System……………………………...………...……..…………………………..…41 3.3.1. Smart Apartment Overview…………...………...……..…..………………………..…41 3.3.2. Sensor Selection…………...………...……..…..……………………………..……..…43 3.4. Summary…………...………...……..…..……………………………..…………….…..…51 4. Data Analytics for the Multi-dimensional Behavioral Model of the Elderly…………..…..…54 4.1. Room Occupancy Monitoring……..…..……………………………..……………..…..…54 4.2. Appliance Usage Monitoring……..…..……………………………..……………..…...…61 4.3. Activity Tracking……..…..……………………………..……………………….....…...…71 4.4. Summary……..…..……………………………..………………………….…….....…...…75 5. Conclusions……..…..……………………………..………………………….……...... …...…83 Reference……..…..………………………………..………..……………………...…….…...…85 Appendix……..…..………………………………..………..……………………...…….…...…94 Publications……..…..………………………………..………..……...…………...…….…...…113
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List of Figures
Figure 1: Unidirectional sensor-based health monitoring system architecture……………...…….8 Figure 2: Bidirectional caregiver-based health communication system…………………….…….9 Figure 3: Overview of the smart and minimum-intrusive monitoring framework for health assessment of the elderly………………………………………...………………...…………….23 Figure 4: Floor plan of maple knoll apartment………….………………………...……….…….42 Figure 5: Conceptual schema for sensor mapping…………………………………………..….44 Figure 6: The Monnit Wi-Fi motion sensor……………………..………………...…….……….46 Figure 7: Innovative energy monitor: Smappee…………………………………………….….47 Figure 8: Fitbit Force tracker………..………………………………………………….…….….49 Figure 9: Synchronized data visualization of Fitbit Force tracker………..…………….…….….49 Figure 10: Sensor installation in maple knoll apartment……………………...…………………51 Figure 11: Procedure of designing the smart and minimum-intrusive monitoring framework...53 Figure 12: iMonnit online wireless sensors portal………………………………………………55 Figure 13: Room occupancy data…...……………………………………………...……………55 Figure 14: Room occupancy data visualization when subject is alone at home.……………57 Figure 15: Room occupancy data visualization when caregiver comes…………………………58 Figure 16: Room occupancy data visualization when subject is absent……………………...….58 Figure 17: Room occupancy features in January………………………………………………...60 Figure 18: Smappee event list of appliance usage………….…………….………...……………62 Figure 19: Appliance usage data visualization when caregiver comes….…………….…………64 Figure 20: Appliance usage data visualization when subject is alone at home………………….64 Figure 21: Appliance usage data visualization when subject is absent………………………….65 Figure 22: Usage pattern of refrigerator…………………………………………………………66 Figure 23: Usage pattern of iron……………..…………………………………..………………67 Figure 24: Usage pattern of washing machine……………………………..………….…………67 Figure 25: Usage pattern of stove………………………………….…………………………….68 Figure 26: Usage pattern of toaster…………………….…………..…………………………….68 Figure 27: Usage pattern of microwave…………………………………………………...……..69 Figure 28: Power consumption data from 01/18/2015 to 01/25/2015………………………...…70
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Figure 29: Fitbit step, distance, and stair data……………………………………..……...……..73 Figure 30: Fitbit total calorie and activity calorie data………………………………...... 74 Figure 31: Fitbit minutes data for different active levels……………………………………….74 Figure 32: Data comparison between normal behavior and recent behavior…………………….76 Figure 33: Behavioral model dimension conversion using factor analysis…………...…………80 Figure 34: Health indices visualization for multi-dimensional behavioral model of the elderly..81 Figure 35: Trend of health status indicator……………………………………...……………….82
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List of Tables
Table 1: Selected patents for review…………………..………………………………………....6 Table 2: Keywords used for Scopus, WOS, and PubMed………………………………....………11 Table 3: Sensor categorization and common examples…………………...……………………..12 Table 4: Behavior modeling methods summary……………………………………………….15 Table 5: Data analysis methods summary...... …...18 Table 6: Measurement scales used in functional healthcare of the elderly…………………...... 35 Table 7: Content of the measurement scales used in functional healthcare of the elderly…...... 38 Table 8: Theoretical multi-dimensional behavioral model…………………………………...... 39 Table 9: Room usage details of maple knoll apartment………………………………………….42 Table 10: Practical data acquisition strategy…………………………………………………….45 Table 11: Feature extraction for room occupancy data………………………………………….59 Table 12: Room occupancy feature value comparison………………………….……………….61 Table 13: Identified appliances using iPhone Smappee App…………………………………….63 Table 14: Fitbit Force features …………………………………………………….…………….72 Table 15: Behavioral item measurement summary…………………………………………….75 Table 16: Useful feature for behavioral items………………………………………………….78
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1 Introduction
1.1 Introduction
With the improvements of the living quality, people in the world are expecting longer and healthier lives. Based on statistical data, there were about 500 million people worldwide who were 65 and older in 2006, and it has been estimated that this number will have a dramatic increase to 1 billion by 2030 [1]. In 2011, the number of people who are 65 and older in United States was 40 million, but this number is projected to more than double to 89 million by 2050. Even more striking is projected acceleration of aging in many developing countries such as India, Mexico, Brazil, and China [2]. This results in that the health of more elderly people will deteriorate as their age increases. It has been reported that of the roughly
150,000 people who die each day across the globe, about two thirds—100,000 per day—die of age-related causes. In industrialized nations, the proportion is much higher, reaching 90%
[3]. Examples of aging-associated diseases are cardiovascular disease, cancer, arthritis, cataracts osteoporosis, type 2 diabetes, hypertension and Alzheimer's disease. The incidence of all of these diseases increases rapidly with aging [4].
As health is becoming an essential expectation of today’s society, are we really ready for the aging world? The societal aging not only motivates the increasing necessity of expanded healthcare services and facilities for the elderly, but also affects economic growth and other issues including the sustainability of families, chronic disease prevention, the ability of states and communities to provide resources for the elderly, and international relations, etc.
An Organization for Economic Co-operation and Development (OECD) study found that the share of total health expenditure attributed to people aged 65 and over ranged from 32 percent to 42 percent, compared with their population share of 12 percent to 18 percent [5].
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The healthcare of elderly people, while preserving/improving their quality of life, will become a critical societal issue in coming years as their relative number increase in the population [1, 6, 7].
1.2 Motivation
Over the past decade, it has been realized to raise the awareness of societal aging and be prepared for providing healthcare systems and services for the elderly. Recent studies indicate that home and community based care for elders is a preferable alternative to institutional care, which not only promotes independent living but also provides tremendous benefits [8]. Several healthcare services and facilities have been built to assist the elderly in their Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL)
[9] to maximize their wellbeing and minimize the risks of illness. For instance, caregivers can be hired to take care of the elderly who suffer from chronic diseases, help them to take medicine in time, meet their daily living needs such as dressing, eating, cleaning and moving, etc. If the elderly is noticed to have any decline in function or acute illness, the caregiver and medical professionals will be notified right away for further diagnosis or treatment such that the just-in-time healthcare services can be provided for the elderly.
However, with the higher developments and requirements of today’s healthcare services for the elderly, some corresponding problems haven’t been thoroughly discussed and solved.
For instance, due to the dramatic growing number of the elderly who require healthcare, the availability of caregivers may be insufficient because the number of people able to provide care will decrease [10]. In addition, having a caregiver to stay at home observing the health status of the elderly all the time could be unrealistic and of high cost. Take Alzheimer's diseases (AD) as an example, the spouse or a close relative is most likely to be the main
2 caregiver of Alzheimer patients [11]. They averagely spend 47 hours every week taking care of a person with AD instead of going to work. Based on the study, the direct and indirect costs of caring for an Alzheimer's patient average between $18,000 and $77,500 per year in the United States [11, 12]. It is obvious that great burdens including social, psychological, physical or economic aspects are placed on caregivers [13]. Moreover, if the caregiver is not well-trained with medical knowledge, it will be more difficult to find the potential and progressive symptoms of aging-associated diseases. The behavioral changes of the elderly are not easily captured if caregiver does not keep an eye on the elderly and communicate with him/her. Therefore, the functional and living ability will continue to deteriorate until the illness develops to its middle or even late stage, which will definitely increase the caring difficulty and financial burdens for their families.
Consequently, to improve the elderly people’s quality of life, lower their healthcare cost, and reduce the reliance on caregivers, an intelligent, minimum-intrusive, and low-cost monitoring system that resides symbiotically with the elderly in their homes needs to be developed in the long run for monitoring behavioral abnormality of the elderly such that they will not have to have a caregiver in their residence. Meanwhile, behavioral related data of the elderly can be measured and analyzed for the assessment of their health. With a continuous monitoring, abnormal behavior will be reported and medical professionals can be alerted for necessary healthcare or treatment.
1.3 Research Objectives
As a feasibility study, the immediate goal of the proposed research is to design a systematic framework of the intelligent, minimum-intrusive, and low-cost sensing system for monitoring behaviors of the elderly and measuring or assessing their health status. The
3 research is expected to yield considerable benefits to the society by developing technologies to help keep elders at homes while improving the quality of their care and safety. An interdisciplinary team of faculty members from College of Engineering and Applied Science
(CEAS), College of Nursing (CON), and College of Medicine (COM) in University of
Cincinnati (UC) has been working together on the idea of establishing an Advanced
Gerontics Engineering (AGE) center.
The research objectives and associated sub-tasks are outlined as follows:
(1) To review the state-of-art behavioral monitoring and health assessment literature and
understand concepts and issues in health measurement of the elderly.
(a) To review the recent patents and scientific papers about healthcare monitoring systems,
sensors, and behavioral monitoring and health assessment methods of the elderly.
(b) To understand the general concepts and issues such as domains of health, commonly
used assessment scales and psychometric properties of health measurement scales of
the elderly.
(2) To develop a multi-dimensional behavioral model of the elderly and design the smart
and minimum-intrusive monitoring system.
(a) To define a behavioral model to be monitored including different dimensions related
to health of the elderly.
(b) To select sensors as well as data acquisition devices and establish the smart and
minimum-intrusive monitoring system.
(3) To extract features and select data analysis algorithms for health assessment of the
elderly.
(a) To extract features from sensor data for future evaluation on reliability, validity, and
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responsiveness for each dimension of behavioral model of the elderly.
(b) To illustrate data analysis methods for multi-dimensional behavioral model and health
assessment of the elderly.
1.4 Dissertation Organization
The remaining of this dissertation is organized as follows. Section 2 presents the issues and state-of-art on healthcare monitoring of the elderly, including patent search on network- based healthcare monitoring systems of the elderly and systematic review of the cutting edge development of behavioral monitoring technologies for healthcare tracking in the elderly population. In Section 3, the development of multi-dimensional behavioral model of the elderly is introduced. Also, the integrated design of the smart and minimum-intrusive monitoring system is illustrated. Section 4 discusses the data analysis for the multi- dimensional behavioral model of the elderly. In addition, some future tasks such as psychometric property testing and overall health assessment of the elderly is planned.
Section 5 draws the conclusions.
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2 Issues and State-of-art on Healthcare Monitoring for the Elderly
2.1 Patent Search on Healthcare Monitoring Systems of the Elderly
To investigate the state-of-art on healthcare monitoring systems of the elderly, forty-one patents (see appendix one) which are searched using Free Patents Online
(http://www.freepatentsonline.com/) from last 20 years in US are reviewed. The keywords used for selecting these patents are (elderly care) or (elder care) in text and (network) and
(health) in abstract. The reason for using these keywords is because we would like to investigate more on network-based, integrated and synchronized healthcare system of the elderly rather than a single assistive device. It is also more desirable to find how healthcare applications for the elderly can be implemented with the help of cable/wireless network, multimedia technologies as well as data analytics. Based on this strategy, the abstracts of all forty-one patents are reviewed first and patent 17, 20, 28, 29, 30 ,33, 34, 35, 36, 37, 38, 41 are excluded because of lower relevance. For the rest of the patents, there are very similar and repeated patents and the latest one is selected to be reviewed among the repeated ones.
As shown in Table 1, nine unique patents are reviewed thoroughly in the end.
Table 1: Selected patents for review
Number Reviewed Patent Repeated Patent (1) 1 1 (2) 2 2 (3) 3 3, 5, 6, 24 (4) 7 4, 7, 8, 10, 12, 13, 14, 15, 16, 21, 22, 23, 26, 27 (5) 11 9, 11 (6) 19 18, 19 (7) 31 31, 32 (8) 25 25 (9) 40 39, 40
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These nine patents are categorized into 3 groups based on their characteristics. Three groups of patents: (1) unidirectional sensor-based health monitoring system; (2) bidirectional caregiver-based health communication system; (3) medication-based system, will be introduced consecutively.
(1) Unidirectional sensor-based health monitoring: patent US 5410471, titled “Networked health care and monitoring system” [14], introduces a networked healthcare and monitoring system capable of providing an updated reliable vital information such as blood pressure, pulse rate, body weight, and so forth on the health condition of individuals and adapted to support home healthcare and maintenance; patent US 8103333, titled “Mesh network monitoring appliance” [15], provides a healthcare monitoring system for a person includes one or more wireless nodes forming a wireless mesh network to communicate data over the wireless mesh network to detect a heart attack or a stroke attack; patent US 7001334 B2, titled “Apparatus for non-intrusively measuring health parameters of a subject and method of use thereof” [16], illustrates an integrated subject monitoring system facilitating measurement, collection and analysis of data pertaining to the health status of a subject including location and mobility, vital signs, and health parameters.
A typical architecture of sensor-based health monitoring system is shown in Figure 1. This type of healthcare system usually utilizes numerous sensors to collect healthcare data from the elderly, such as vital signs, motion data and environmental data, for analysis. The sensors could be deployable sensors like pressure sensors on mattress to capture the sleeping pattern, weight sensors on chairs to measure body weight, etc. Wearable sensors are also available for ECG, blood pressure and glucose level data acquisition. All the data collected can be either stored in local data center or transmitted to remote data analyzer through cable
7 or wireless network. The remote data analyzer can compare the incoming data pattern with pre-established baseline to determine whether any abnormality happens and whether elderly person needs immediate medical attention from healthcare providers. It can be seen that this system does not require the involvement of healthcare providers all the time. Healthcare providers or other medical professionals will only be notified when abnormalities happen.
Therefore, the working flow is usually unidirectional for this type of healthcare monitoring system.
Figure 1: Unidirectional sensor-based health monitoring system architecture
(2) Bidirectional caregiver-based health communication system: a remote caregiver support system which allows caregivers to provide informal care to care receivers using video cameras is introduced in the Patent US 7808391 B2, titled “Remote caregiver support system ” [17]; patent US 8489428, titled “Remote health monitoring and maintenance system” [18], describes a system that enables a healthcare provider to monitor and manage health condition of a patient. Instead of communicating using video cameras, this system has a healthcare provider apparatus operated by a healthcare provider and a remotely programmable patient apparatus that is operated by a patient; an ambulatory (in the home) patient health monitoring system is disclosed in Patent US 5544649, titled “Ambulatory patient health monitoring techniques utilizing interactive visual communication” [19]. Both patient’s residence and physician’s office are equipped with video cameras and TV display so the simultaneous interactive communication is enabled; patent US 7185282, titled
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“Interface device for an integrated television-based broadband home health system” [20], discloses an integrated television-based broadband home health system, including patient station, healthcare provider station, caregiver provider station and emergency response provider station; patent US 5390238, titled “Health support system” [21], provides healthcare support method and apparatus which economically provides medication control, wellness checking and patient data accumulation and reporting capability.
Figure 2 depicts the typical framework of bidirectional caregiver-based health communication system. Basically, the simultaneous interactive communication is enabled because both patient’s residence and caregiver’s office are equipped with video cameras and
TV display or other media systems. Meanwhile, the sensors read data from elderly person and then data can be analyzed before results delivery to caregiver. The transmission of video signal and data between elderly person and caregiver is realized by communication network.
Feedback can be provided by caregiver to elderly person so the communication between them is bidirectional.
Figure 2: Bidirectional caregiver-based health communication system
(3) Medication-based system. As the healthcare system moves from an illness model to a wellness model, gaining enhancements in prevention and managed care as well as new entitlements and expanded drug-benefit coverage, the role of medications will be pivotal
[22]. Correspondingly, more advanced medication-based systems with embedded
9 monitoring devices have been off the shelf. These systems can not only dispense medicine based on patient’s prescription, but also monitor the medication adherence and health condition of patients. As a result, the self-management medication treatment can be realized by adjusting prescription if the health condition of patients has changed.
Patent US 7369919, titled “Medication adherence system” [23], describes a system comprising a medication tray and a docking station for facilitating effective self- management of medication treatment by patients. The medication tray accepts medication filled containers and mates with the docking station. The medication tray receives prescription data at the time when medication tray accepts the medication filled containers, which is then downloaded to the docking station. The docking station monitors and reports to third parties, via a network, a patient's compliance with various medication treatment regimens.
From this patent search we can see that the difficulty of healthcare monitoring for the elderly has been dramatically decreased by sensing the health parameters, interactively communicating with elderly and monitoring their medication adherence with development of new assistive devices and intrusive or non-intrusive monitoring technologies [24]. Several patents have established an integrated healthcare network with the involvement of vital sign sensors and healthcare service, which are beneficial for improving the efficiency to receive medical attention after abnormal conditions. The patents will be very helpful to provide guideline in building the framework of the smart and minimum-intrusive monitoring system, but it is necessary to select sensors to monitor the behavioral changes related to the health status of the elderly specifically.
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2.2 Literature Review on Healthcare Monitoring of the Elderly
To systematically review the cutting edge development of behavioral monitoring technologies for healthcare tracking in the elderly population, including sensors, behavior modeling strategies, and data analysis methods used in a variety of applications, We undertook systematic searches of Scopus, Web of Science (WOS), and PubMed. The reason to use these databases is because Scopus and WOS are the most comprehensive databases on different scientific fields which are frequently used for literature search [25]. And PubMed has an extensive library of bio-medical literature citations and abstracts in the fields of medicine, nursing, health care systems, preclinical sciences, dentistry, and veterinary medicine [26]. The keywords used are listed in Table 2 for each database. No date restrictions were placed on the searches and all relevant papers were included if they are written in English. The electronic searches are up to date on Aug. 31, 2014.
Table 2: Keywords used for Scopus, WOS, and PubMed Database Keywords (elder or elderly or senior) in Article Title, Abstract, Keywords Scopus AND (behavior or behavioral) in Article Title, Abstract, Keywords AND (detection or monitoring or sensing) in Article Title (elder or elderly or senior) in Topics WOS AND (behavior or behavioral) in Title AND (detection or monitoring or sensing) in Title (elder[Title/Abstract] OR elderly[Title/Abstract] OR senior[Title/Abstract]) PubMed AND (behavior[Title] OR behavioral[Title]) AND (monitoring[Title] OR sensing[Title] OR detection[Title])
As previously mentioned, this systematic review is about the cutting edge development of behavioral monitoring technologies for healthcare tracking in the elderly population. It is also more desirable to find how healthcare monitoring applications for the elderly can be implemented with sensing technologies as well as data analytics.
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Using the query in Table 2, 289, 197, and 6 papers were found in Scopus, WOS, and
PubMed, respectively. Firstly two reviewers independently looked at the titles of all papers
and agreed whether they should be included or not. Secondly the abstract of a paper was
read if we could not tell its relevance by looking at the title. Eventually 126 Scopus papers,
27 WOS papers and 3 PubMed papers were included, and 130 unique papers were selected
and reviewed after removing duplicates.
2.2.1 Sensors
The rapid development of sensing technologies has leveraged its contribution in monitoring,
maintaining and improving the healthcare for the elderly. A large amount of sensors have
been used in several ongoing healthcare monitoring projects. Table 3 shows the
categorization of four types of sensors found in the literature review with some common
examples.
Table 3: Sensor categorization and common examples Category Examples Pyroelectric infrared sensor [27-68] Motion-based sensors Accelerometer [47, 58, 69-87] Door/magnetic sensor [33, 34, 38, 43, 45, 46, 48, 50-53, 56, 57, 67, 88-92] Pressure/contact sensor [34, 39, 44, 47, 50, 52, 55, 57, 88, 89, 91-94] Vision-based sensors Video sensors [56, 80, 82, 89, 93, 95-111] Cameras [47, 55, 57, 70, 112-118] Appliance usage sensors Appliance sensing units/power meters [28, 30, 33, 41, 42, 89-91, 94, 119- 125] Temperature sensors [33, 38, 39, 43, 44, 46, 50, 51, 55, 56, 87, 93, 126] Smoke sensors [50, 93] Environmental sensors Water flow sensors [33, 56, 89, 127] Humidity sensors [39, 51, 87] Light sensors [39, 44, 51, 128] Carbon dioxide sensor [38, 43, 56]
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(1) Motion-based sensors. In order to monitor the indoor movement, location and safety of an elderly, a large variety of sensors including pyroelectric infrared (PIR) sensors, accelerometers, door/magnetic sensors and pressure/contact sensors are used in several ongoing healthcare monitoring projects for the elderly. For instance, Moon et al. calculated daily average step count of an elderly using PIR sensors and door magnetic sensors [48].
Location and stay duration data was recorded in [59] using motion detectors for efficient discovery of living habits of an elderly. Dovgan et al. collected movement pattern data of elderly people and used Xsens accelerometers for fall detection [58]. Accelerometer embedded in the mobile phone was also utilized in [72] to characterize physical activity and time domain features were extracted to test gait and balance. Pressure/contact sensors in bed and chair were often used together with motion sensors for more accurate room occupancy data acquisition [47, 52, 55].
(2) Vision-based sensors. Facilitated by information and communication technology (ICT), the technological evolution of video monitoring and surveillance systems has been ongoing over recent years, now includes remote telecare systems using the latest computer vision technology [107]. Due to real-time video signal transmission and ease of capturing human behaviors, vision-based sensors have been widely used in healthcare monitoring projects for the elderly. Video cameras recorded video events for detecting 3D human postures and tracking people moving in the scene [89]. A surveillance video system was proposed in [100] for patient posture recognition, movements monitoring, and fall detection. The system developed in [115] consists of some multi-camera gates to acquire corridor passer events and a processor for face recognition and behavior detection or estimation. In [56], video- based monitoring in real-time was implemented to recognize motion-based human actions,
13 like walking, running, falling, bending, standing up, sitting down, as well as more complex activities of daily living.
(3) Appliance usage sensors. In some healthcare systems, appliance usage pattern sensors turned out to be useful for detecting the on/off status of household appliances so that daily activities of an elderly on appliance usage can be known. Appliance usage related features such as current frequency, level and position of peaks could be identified for matching appliance usage patterns [121]. Survadevara et al. collected appliance usage data for the purpose of understanding all the important elderly people actions such as preparing breakfast/lunch/dinner, bathing, bathroom use, dinning, sleeping and self-grooming [94].
The long-term average data of TV usage on weekdays and holidays were compared and watching TV was identified as one of the major in-home activities of the resident [42].
(4) Environmental sensors. Different kinds of environmental sensors have been deployed in the residence to maintain its safety and maximize the wellbeing of the occupying elderly.
These include carbon dioxide monitors, flood detectors, smoke detectors, temperature extremes sensors and smart wave/stove sensors to send alerts and notify residents when abnormalities happen [38, 39, 129, 130]. In addition, some environmental sensors were also used to monitor room occupancy and daily activity of an elderly. For instance, carbon dioxide sensors [43, 46] and light sensors [128] were used to detect the presence/absence of a subject in a room. Cooking could be detected using temperature sensors installed in the kitchen [46].
2.2.2 Behavior Modeling
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Using different kinds of sensing technologies, various behaviors of the elderly were
modeled and monitored in most papers included in our literature review. Three typical types
of behavior modeling methods, such as room occupancy and motion, household appliance
usage, and task-related behavior, are summarized in Table 4.
Table 4: Behavior modeling methods summary Behavior modeling method Sensors Data/features - PIR sensors Presence/absence in a room [27, 33, 35, 38, 43, 50, 60- - Pressure sensors 63, 67, 68, 131] - Door sensors Room location, start time, and duration [29, 49, 52, 54, - Light intensity 59, 128, 132] sensors Activity level, mobility level, non-response interval Room occupancy and - Carbon dioxide [64] motion sensors Long periods of inactivity, lacking activity, unusual - Vision sensors presence [52] - Accelerometers Bed, couch, toilet chair usage, etc [91] Open/close doors [33, 38, 43, 48, 133] Walking, sleeping, sitting, standing, watching TV, etc [58, 89, 96-99, 118, 126, 130, 134] Bodily trajectory/movement [38, 40-42, 45, 46, 63, 65, 66, 70, 74, 75, 90, 100, 105, 112, 116, 123, 130, 135- 140] - Appliance Non-usage or inactive duration of the appliances [119] sensing units Over-usage of household appliances [119] Household appliance usage - Current On/off status of appliances [28, 41, 42, 46, 90, 91, 94, transducers 120-125] Frequency, level and position of current data [121] - Motion sensors Forgetting to turn gas off/turn tap off, bath, toilet, - Door sensors wake-up, retiring [53] - Video cameras Wake up time, meal preparation, medication adherence, - Accelerometers overnight toileting, general activity [141] Task-related behavior - Flame sensors Gait data [142] - Carbon dioxide Task execution time, walking speed, step length [10] sensors Number of door openings, the length of sleep, absences from the house, use of a cooking stove, and time spent watching television [30]
(1) Room occupancy and motion. PIR sensors and door sensors are typically used to detect
the presence/absence of a person in different rooms and location by recording the start time
15 and stay duration. For instance, Moon et al. used PIR motion sensors and door magnetic sensors to detect elders’ behaviors and examined their calorie expenditure [48]. Hsu et al. collected room occupancy data and built a personalized model of normal behavior.
Abnormal behaviors like falling (staying in a place for too long) and not going to toilet for all day could be detected and an alarm could be raised by the system [61]. Shin et al. extracted three features including activity level (how many times the subject’s motion was captured by the motion sensor), mobility level (movement throughout his/her house), and non-response interval (NRI) (a measure of the time between the subject’s motions) from the measured motion signals. Abnormal behavioral samples were simulated with events and thresholds [64]. Shieh et al. proposed a method that used the average distances (i.e., similarity between readings from m-bit IR sensors) and changed distances as an important index for diagnosis when a person’s living pattern and health has deteriorated [62]. PIR motion sensors or bed/sofa pressure sensors are used in [52] to collect location, time and duration data of an activity for any unusual presence, unusual absence, unusual long inactivity, not present when should be, unusually short activity, and changes in day rhythm.
In addition, various sensors including vision-based sensors [89, 96-99], motion-based sensors [58, 89, 126], environmental sensors [89, 130] were used to monitor motions such as walking, sleeping, sitting, and watching TV. Similar to room occupancy monitoring, some researchers [112, 116, 130, 135] also characterized bodily trajectories/movements in living area as behaviors in case of detecting some normality violation.
(2) Household appliance usage. Appliance electrical status sensors are usually used to identify patterns of residents’ behavior based on the correlation with operating appliances.
Usage time and status of appliances including hot water kettle, microwave oven, toaster,
16 television, room heater, and washing machine can be recorded to establish a daily behavior routine. Deviations from normal behavioral patterns will trigger an alert to caregivers for such deviations, which could reflect changes in health status of the elders who live alone.
For example, Suryadevara et al. developed an intelligent, robust, less cost, flexible and real- time home monitoring unit to record the basic home activities and respond immediately when there is a change in regular daily activity of elders based on their appliance usage routine [120]. A mechanism for estimation of well-being condition of the elderly based on usage of household appliances was reported in [91]. The developed system for monitoring and evaluation of essential daily activities was tested at the homes of four different elderly persons living alone and the results are encouraging in determining wellness of the elderly.
Kushiro et al. used current sensors to detect operation of appliances with thorn like peak of electrical current generated by their working appliances, and identified patterns of residents' behavior based on the correlation of operating appliances [121]. In [94], appliance usage data for important elderly people actions such as preparing breakfast/lunch/dinner, bathing, sleeping and self-grooming is used for wellness model monitoring of the elderly in smart home.
(3) Task-related behavior. There are also some specific behaviors and tasks being defined and monitored using a combination of different sensors in the literature. Abe et al. defined several task-related behaviors such as forgetting to turn gas off/turn tap off, bathing, toileting, waking-up, and retiring to bed [53]. A QuietCare® system was configured and used as a behavioral monitoring system in [141] to collect data on: 1) wake up time; 2) meal preparation; 3) medication adherence; 4) overnight toileting; and 5) general activity. In [30], the number of door openings, the length of sleep, absence from the house, use of a cooking
17
stove, and time spent watching television were monitored and the collected data was
visualized. Three-axis accelerometer sensor was foot-mounted for both groups of
cognitively healthy elderly and the group of elderly with light or mild dementia. Results had
shown that it is possible to differentiate between healthy and suffering from dementia
subjects [142]. Crispim-Junior et al. developed several scenarios to monitor the IADL using
2D video camera and gait information using accelerometer for both normal elderly and
elderly with Alzheimer’s disease (AD). Significant differences could be found between the
control group and AD patients in terms of feature values such as walking speed, task
duration, number of finished tasks, and number of omitted tasks [10].
2.2.3 Behavior Data Analysis
Data analysis methods are usually applied after behavior data acquisition to digest the data
and infer the living pattern or wellbeing of the elderly from an objective point of view. In
the literature, a number of intelligent data analysis methods were used for the purposes of
signal processing, abnormal detection, behavior recognition, and prediction, which have
been summarized in Table 5.
Table 5: Data analysis methods summary Category Algorithms Wavelet transform [121] Signal processing Infinite impulse response filters or Haar wavelets [143] Short-time Fourier transform [105] Principal component analysis [137] Fuzzy C-means [29] Support vector machine [55, 60] K-means clustering [31] Hidden Markov model [52, 112, 137] Euclidean distance [62] Abnormality detection Bayesian network [97, 102] Area under curve [128] Self organizing map [52] Multi-layer perception neutral network [118, 136]
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Two-dimensional probabilistic density function [40] Multiple instance learning [55] Nearest neighbor [55] Manhattan distance [56] Bayesian network [75, 119, 122] Gaussian mixture model [49, 77] Hierarchical hidden Markov model [93, 144] Hidden Markov model [41, 60, 125, 126, 145] Behavior recognition K-means clustering [41, 122, 146] Fuzzy neural network [47] Support vector machine [58, 70, 82] Decision tree [70] Multi-layer perception neutral network [39, 101] Prediction Linear regression [48] Autoregressive moving average [94]
(1) Signal processing. Signal processing algorithms are usually used to transform a great
amount of raw data collected in behavior monitoring applications for the elderly into useful
information describing behavior pattern of the elderly such that the vast amount of raw data
can be simplified into a set of features representing the characteristics of behaviors for the
elderly. Several signal processing methods have been applied in the literature. Wavelet
transform method was used in [121] to extract features (frequency, level and position) from
current data for detecting operation of appliances. The experimental tests in [143] showed
that compact algorithms based on nearest neighborhood classifiers and filter banks with
Infinite Impulse Response (IIR) filters or Haar wavelets can identify the state of the
furniture user in the form of postures and activity levels. Doukas used Short-time-Fourier
Transform (STFT) for detection of sounds features characterizing the fall of the human body
or the vocal stress in speech expressions indicating distress events [105]. In [137], Principal
Component Analysis (PCA) was used to reduce the sequence matrix to 2D space defined by
the first and second PCA vectors, which were used to judge the similarity between unlabeled
sequences and also to identify outliers.
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(2) Abnormality detection. After the representative features are obtained, intelligent abnormality detection algorithms can be used to evaluate whether the health status of an elderly has worsened or abnormalities have happened so further actions such as alerting healthcare professionals, providing medication prescription and treatment can be taken to minimize the health risks [147-149]. A health index or distance can be used to quantify the health status of an elderly person by analyzing the data pattern similarity between baseline behavioral model and recent behavioral model of the elderly. For instance, the average distance or similarity between readings from m-bit PIR sensors in [62] acted as an important index for diagnosing when a person’s living pattern and health have deteriorated. In [56], the
Manhattan distance had been chosen to compare features extracted using image processing for abnormal events detection. Popescu et al. benchmarked Multiple Instance Learning
(MIL), k-Nearest Neighbor (kNN) classifier and Support Vector Machine (SVM) to classify
'good day' or 'bad day' characterized by motion, restlessness, pulse and breathing for early illness recognition [55]. Self Organizing Map (SOM) and Hidden Markov Model (HMM) were also used to detect abnormalities such as long periods of inactivity, lacking activity, unusual presence and changes in daily activity patterns [52].
(3) Behavior recognition. The behavioral features of the elderly extracted from signal processing could also be fed into some supervised or unsupervised algorithms so different behaviors of an elderly can be ‘learned’ in the training process. When a new set of features comes in, it can be tested to see which behavior pattern it matches best such that the incoming behavior can be identified or recognized. For example, Bayesian network was trained to classify bodily movements like walk, sit, stand, and lie [75], daily activities like cooking meals, watching television, and preparing tea [119, 122]. Gaussian Mixture Model
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(GMM) was applied to the sensor data to develop a probabilistic model of event types which could represent underlying behavioral activities of the subject. Identifiable events include sleep behavior, changing clothes, bathroom/toilet use, leaving/returning home, and meal preparation in [49], and walk, run, climb up, climb down, vacuum, rush, sit-ups in [77]. In order to recognize not only a state but also behavioral patterns expressed by the state sequences, HMM was trained in several behavioral monitoring applications for appliance usage [41, 125] and daily activities [60, 126, 145]. As a supervised algorithm, SVM was used to classify three gestures (beckoning hand, pointing hand or miscellaneous) and running, walking, car driving, or cycling (outdoor) in [70], and four health problems
(tripping, fainting, falling from the chair, others) in [58].
(4) Prediction. There were also a small number of applications in which time-series models and regression models were trained using historical data. For instance, Survadevara et al. trained Autoregressive Moving Average (ARMA) model using sleeping observations of first eight weeks to predict sleeping durations in the ninth week. A calorie expenditure model was presented in [48] with a remote activity monitoring system. The proposed method was used to describe the daily activity patterns of older adults and examine the relationships between physical activity and calorie expenditure using linear regression model.
2.3 Summary
From the patent and literature review it can be seen that numerous efforts have been targeted on the development of healthcare monitoring system for the elderly. First, various newly developed sensors such as motion-based sensors, vision-based sensors, appliance usage sensors and environmental sensors have been leveraged to provide functions and benefits for behavior monitoring for the elderly. Second, it is exciting to see all kinds of behavior
21 models defined by a diversity of monitoring strategies including room occupancy and motion, household appliance usage, task-related behavior. And there are quite a few more detailed behavior monitoring methods under each of the three main categories. Third, intelligent data analysis is playing a more and more significant role in behavior monitoring applications for the elderly. Based on our review, 41.5% (54/130) of the reviewed papers more or less used intelligent data analysis algorithms for abnormality detection or behavior recognition.
Although the field of behavior monitoring is extremely active with a very wide range of approaches currently being investigated, none of these started from a systematically and rigorously defined behavior model related to health of the elderly. What these researchers have done was basically to take whatever is convenient to measure and called that the
“behavior” of the subject. They then used various modeling techniques to detect “abnormal behavior”. When “abnormal behavior” is detected then the subject is deemed to have a change in health status. In other words, the behavior model is defined based on the availability of sensors rather than the relationship with health status. This approach is basically a pure engineering approach with no medical knowledge support behind it.
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Figure 3: Overview of the smart and minimum-intrusive monitoring framework for health
assessment of the elderly
In this research, a systematic framework of the intelligent, minimum-intrusive, and low-cost sensing system for monitoring behaviors of the elderly and measuring or assessing their health status (see Figure 3) is proposed and designed to fill the existing gaps:
(1) Lack of a well defined behavior model related to health of the elderly. As previously discussed, a formal, comprehensive, health related multi-dimensional behavioral model does not exist in the literature. Without such a model, any efforts in improving health outcomes for the elderly using smart home technologies would be ad hoc and cannot be expected to provide consistent results. Medical knowledge should be applied in order to validate that behavior pattern change correlates to health status change of the elderly.
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(2) Lack of a link between monitored data and health status assessment and inference.
Multi-dimensional behavior data is collected in the elderly population using low-cost and minimum-intrusive sensors. Intelligent data analysis algorithms should be chosen and applied to mine the inherent information hidden in the data to assess the health status of the elderly.
To fill the gaps, a monitoring system is proposed as shown in Figure 3. If realized, the system would reside symbiotically with elderly people or individuals in their homes to detect early health decline for timely treatment. Ultimately, it will provide considerable benefits to the society by improving elders’ quality of life, lowering healthcare cost, and reducing their reliance on caregivers.
This research has three major contributions. The first is the smart and minimum-intrusive monitoring framework that can truly measure health status of the elderly. As discussed above, a formal, comprehensive, health related behavioral model does not exist in the literature. Without such a model, any efforts in improving health outcomes of the elderly using smart home technology would be ad hoc and cannot be expected to provide consistent results. Our framework would be a valuable tool for other researchers to enable systematic design of monitoring strategies and techniques that target on improved health outcomes. The second contribution is feature extraction of sensor data from the multi-dimensional behavioral model of the elderly. The collected raw data will be transformed into useful information describing behavioral pattern of elderly such that a clean, simplified, well arranged feature matrix can be used by medical professionals without delving into tons of raw data for the purpose of testing and health status analysis. The third contribution is the derivation of health assessment scales using intelligent data analysis methods in engineering
24 field. Instead of inferring the health of an elderly to be only normal or abnormal, intelligent data algorithms are able to output numerical scales ranging from 0 to 1 and health radar chart allows the visualization of health indices of the elderly so healthcare decisions can be prioritized by medical professionals. From the interdisciplinary point of view, the three contributions of this research bridge the fields of engineering and medical science together effectively for health assessment of the elderly.
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3 Development of Behavioral Model of the Elderly and Integrated Design of the Smart
and Minimum-intrusive Monitoring System
3.1 Concepts and Issues in Health Measurement of the Elderly
3.1.1 Health Domains of Elderly and Commonly Used Assessment Scales
Understanding the health domains of the elderly and corresponding assessment scales is crucial and indispensable for designing the framework of the smart and minimum-intrusive monitoring system for health assessment of the elderly. Assessment instruments used in healthcare of the elderly cover a wide range of areas relevant to the health and well-being of elderly people. In clinical practices, the following five main areas are included to evaluate an elderly person’s health: physical health, functional health, mental health, quality of life, and socioeconomic status [150]. The content and commonly used assessment scales of these areas are introduced as follows:
(1) Physical health. Physical health is defined as the condition of your body. Good physical health is when your body is functioning as it was designed to function. Lifestyle, human biology, environment, healthcare services are the four main categories of things that affect physical health [151]. Most clinicians quantify physical health or disease by compiling a problem list of defined diagnoses and symptom complexes. There are a number of detailed, disease-specific quantitative or qualitative scales available. Examples of disease-specific scales include the following:
a) Hoehn and Yahr Scale and Webster Scale for Parkinsonism.
b) National Institutes of Health Stroke Scale, Scandinavian Stroke Scale and Canadian
Stroke Scale for stroke.
(2) Functional ability. In the elderly, measurement of functional ability is an essential part of
26 clinical practice. Activities of daily living (ADL) and Instrumental activities of daily living
(IADL) are included in the major domains of functional ability. ADL are defined as the things we normally do...such as feeding ourselves, bathing, dressing, grooming, homemaking, and leisure [152]. And IADL are the activities often performed by a person who is living independently in a community setting during the course of a normal day, such as managing money, shopping, telephone use, and travel in community, housekeeping, preparing meals, and taking medications correctly [153]. Basically ADL are disability oriented while IADL assess handicap rather than disability. There are numerous functional ability scales in the literature include the following:
a) Barthel Index
b) Katz ADL scale
c) Lawton ADL scale
d) PULSES Profile
e) Northwick Park ADL scale
f) Functional Independence Measure (FIM)
g) Frenchay Activities Index
h) Nottingham Extended ADL Scale
i) Duke Activity Status Index
j) Bristol Activities of Daily Living Scale
Healthcare professionals can detect problems in daily activities and determine the type of assistance needed by the person who is being assessed with the help of these functional scales. Impairment of basic activities indicates that caregiver or nursing home support is needed for survival.
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(3) Mental health. Mental health describes a level of psychological well-being, or an absence of a mental disorder. Mental health may include an individual's ability to enjoy life, and create a balance between life activities and efforts to achieve psychological resilience
[154]. It is a significant part of the assessment of patients with dementia. The corresponding scales are used to assess the cognitive ability of older people as well as their behavioral competence. Some tests for mental health assessment are listed below:
a) Abbreviated Mental Test (AMT)
b) Mini Mental State Examination (MMSE)
c) Hamilton Depression Rating Scale
d) Zung Self-rating Depression Scale
e) Clifton Assessment Procedures for the Elderly (CAPE)
(4) Quality-of-life assessment. The term quality of life (QOL) references the general well- being of individuals and societies. Standard indicators of the quality of life include not only wealth and employment but also the built environment, physical and mental health, education, recreation and leisure time, and social belonging [155]. A global measurement of change in a person’s QOL before or after intervention has been gaining its popularity in drug trials. The overall health behavior of the population is usually evaluated by QOL scales.
Easy to be used, easy to be understood, and having capacity to detect change are the three main properties of a good QOL instrumentation. Examples of QOL scales include the following:
a) Short Form 36 (SF-36)
b) Nottingham Health Profile
c) Sickness Impact Profile
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d) Philadelphia Geriatric Center Morale scale
(5) Socioeconomic status. Socioeconomic status (SES) is an economic and sociological combined total measure of a person's work experience and of an individual's or family's economic and social position in relation to others, based on income, education, and occupation. When analyzing a family's SES, the household income, earners' education, and occupation are examined, as well as combined income, versus with an individual, when their own attributes are assessed. Assessment instruments [156] have been devised to monitor
SES such as:
a) Duncan Socioeconomic Index
b) Nam-Powers Occupational Status Score
c) Household Prestige Scale
d) CAPSES
e) Cambridge Social Interaction and Stratification Scale
f) National Statistics Socioeconomic Classification
In this research, it is not necessary to cover every aspect of the elderly person’s health due to the properties of our data-driven based method as well as the complexity of the monitoring system framework. Also, we would like to find some mentally healthy elderly people to be monitored because monitoring both mentally healthy elderly people and people with dementia can disorder our data. The Montreal Cognitive Assessment (MOCA) [157] scale can be used to find out who have mental problems and who don't. More specifically, our research is focusing on functional health of the elderly. The reason is that functional ability among the five main areas of health status definition is a dominant and incorporated area over the others because health decline in other areas such as physical health, mental health,
29 and QOL can be reflected in functional ability.
3.1.2 Psychometric Properties of Health Measurement Scales
Measuring the health status of elderly helps to guide clinical decisions and make plans of care. It also helps to predict which elderly patient will benefit most from a particular intervention and to document whether the patient improves after the intervention is provided
[158]. The quality of information provided by the measurements/instruments depends on the psychometric properties of them, namely, reliability, validity, and responsiveness [159, 160].
A good measurement/instrument needs to have high reliability, validity, and responsiveness to ensure that the information provided is meaningful and effective, regardless whether scale-based or data-driven based instruments are used.
3.1.2.1 Reliability
Usually, better ability to obtain the same result with repeated measurements means a higher reliability of an instrument or scale. In other words, reliability is the ability to produce consistent results on a number of different occasions. Reliability is one of the most important elements of instrument quality. It has to do with the consistency, or reproducibility, or an examinee’s performance on the instrument [161]. Reliability can be judged in the following aspects:
(1) Test-retest reliability. It is the ability measuring if consistent results following administration of the instrument/scale can be achieved on different occasions to the same population. Scale-based measures (self-report) that require individuals to respond to a series of written questions should be examined for test–retest reliability. Problems with the test– retest reliability of self-report measures are most often due to problems with the wording of
30 items when instruments developed for use in one country are used in another [159, 162, 163].
For the proposed data-driven based health assessment approach, test-retest reliability will be examined to see if similar results are obtained within repeated measurements.
(2) Internal consistency reliability. Internal consistency is a measure of the extent to which all of the items in the outcome measure address the same underlying concept. Internal consistency is a desirable characteristic for measures such as the depression scales, for which all the items should deal with depression. Internal consistency is not necessary or desirable for measures such as the SF-36 that intentionally address more than one concept
[159, 162, 164, 165]. It is also not necessary for the proposed research because a multi- dimension behavioral model will be built and monitored.
(3) Rater reliability. Rater reliability includes intra-rater reliability meaning that how consistently a rater administers and scores an outcome measure and inter-rater reliability describing how well two raters agree in the way they administer and score an outcome measure [159]. Using data mining method to assess health status of elderly is a performance based outcome measure, because we act as data analysts or raters to analyze the data and infer/assess health status of elderly. Intra-rater reliability should be examined to see if we consistently administer the data analysis procedure.
3.1.2.2 Validity
Reliability is a necessary but not sufficient property of an instrument or scale. The extent to which an instrument or scale accurately measures the underlying concept or whether the measure chosen achieves its intended purpose is also a crucial property, namely, validity
[150]. There are several aspects of validity:
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(1) Face validity. Face validity is the extent to which a test appears to measure what it is intended to measure. It can be evaluated only subjectively. This type of validity would be important if people are advocating for the universal use of a particular test by a clinical community. Such a test would need to appear to measure what the clinicians in the community intended to measure [159].
(2) Content validity. It is the degree to which a test includes all the items necessary to represent the concept being measured. It can be evaluated only subjectively. For instance, a measure that had good content validity for the young athletic population (dealing with running, jumping, and climbing) would have very poor content validity for the older population (dealing with toilet transfers and ambulation with an assistive device) in the assisted living facility [159].
(3) Criterion validity. To examine this aspect, the results of the instrument or target test can be compared to a gold standard or criterion test. If the target test measures what it is intended to measure, then its results should agree with the results of the gold standard criterion test. It can be examined objectively. The primary problem with criterion validity is that it requires an established gold standard test. There are very few situations in rehabilitation where such a gold standard test exists [159]. After developing the multi- dimensional behavioral model of the elderly in this research, it can be found out that if each dimension has a corresponding gold standard test. If so, the results of data-driven based health status measurement instrument can be compared with those of the gold standard test.
(4) Construct validity. Construct validity reflects the ability of a test to measure the underlying concept of interest to the clinician or researcher. It can be examined objectively.
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This approach is based on the assumption that if we give the test to two groups of subjects that we know differ on the construct of interest, the test scores of the groups should differ if the test actually measures what it is supposed to measure. For example, to examine the construct validity of the 6-minute walk test as a measure of functional mobility in the elderly, the test results of a group of frail elderly individuals who reside in an assisted living facility can be compared to those of a group of healthy elderly who volunteer at the local community hospital. These two groups would be expected to differ in terms of functional mobility [159]. For the proposed multi-dimensional behavioral model of the elderly in this research, construct validity should be examined if a related gold standard does not exist in that dimension. If construct validity is high, it means what we measure is highly related to the health status of elderly.
(5) Convergent validity. Convergent validity is demonstrated when scores on the instrument being examined are highly correlated to scores on an instrument thought to measure similar or related concepts. One example could be that scores on a gait index should be correlated to scores from an activity limitation measure because the concepts of gait and activity limitation are related [159].
(6) Discriminant validity. It could be demonstrated when scores on the test being examined are not correlated to scores on a test meant to measure a very different construct. For instance, the scores from an intelligence test should not correlate with the scores from a measure of activity limitation. If the activity limitation tests were strongly correlated to the intelligence test, it could be argued that the activity limitation test was not measuring the intended construct [159].
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3.1.2.3 Responsiveness
Responsiveness is defined as how capable for an instrument or scale to detect changes related to time or interventions. It includes two types of responsiveness: internal responsiveness and external responsiveness.
(1) Internal responsiveness. Internal responsiveness is defined as the ability of a measure to change during a pre-specified time frame. Internal responsiveness is often examined by administering a measure before and after a treatment of known efficacy [159].
(2) External responsiveness. External responsiveness reflects the extent to which changes in a measure relate to changes in other measures of health status [159].
For the proposed data-driven based health status measurement, responsiveness would be good if sensors measure accurately and results update quickly enough because changes can be reflected on data value since data-driven based method is objective.
As described above, psychometric properties of health measurement provide a great guideline for developing the systematic framework of the intelligent, minimum-intrusive, and low-cost sensing system for monitoring behaviors of the elderly and measuring or assessing their health status. None of the existing literature has ever considered the psychometric properties of health measurement in its monitoring system. Instead, our research proposes a multi-dimensional behavioral model of the elderly in which the psychometric properties are required to be evaluated in each dimension to ensure the quality of health status assessment instrument for the elderly.
3.2 Measurement Scales Used in Functional Healthcare and Behavioral Model of the
Elderly
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3.2.1 Measurement Scales Used in Functional Healthcare of the Elderly
As discussed earlier, functional health of elderly would be focused on in this research because functional ability among the five main areas of health status definition is a dominant and incorporated area over the others because deterioration in other areas such as physical health, mental health, and QOL can be reflected from functional ability. We reviewed the most commonly used measurement scales or instruments in functional healthcare of the elderly. Table 6 lists the 10 measurement scales we found.
Table 6: Measurement scales used in functional healthcare of the elderly Number Measurement Scales 1 Barthel Index of Activities of Daily Living 2 Katz Index of Independence in Activities of Daily Living 3 The Lawton Instrumental Activities of Daily Living Scale 4 Functional Independence Measure 5 Northwick Park Index of Independence in ADL 6 The Frenchay Activities Index 7 Nottingham Extended ADL Scale 8 PULSES Profile 9 Duke Activity Status Index 10 Bristol Activities of Daily Living Scale
The Barthel Index of ADL (see Appendix 3) is an ordinal scale used to measure performance in activities of daily living (ADL). It has been used extensively to monitor functional changes in individuals receiving in-patient rehabilitation, mainly in predicting the functional outcomes related to stroke. It has concurrent, predictive validity and high inter- rater reliability (0.95) and test–retest reliability (0.89) as well as high correlations (0.74–0.8) with other measures of physical disability [166, 167].
The Katz Index of Independence in Activities of Daily Living (see Appendix 4), commonly referred to as the Katz ADL, is the most appropriate instrument to assess functional status as
35 a measurement of the client’s ability to perform activities of daily living independently.
Clinicians typically use the tool to detect problems in performing activities of daily living and to plan care accordingly. It has consistently demonstrated its utility in evaluating functional status in the elderly population. Although no formal reliability and validity reports could be found in the literature, the tool is used extensively as a flag signaling functional capabilities of older adults in clinical and home environments [168].
The Lawton Instrumental Activities of Daily Living Scale (IADL) (see Appendix 5) is an appropriate instrument to assess independent living skills. The instrument is most useful for identifying how a person is functioning at the present time and for identifying improvement or deterioration over time [169, 170]. The validity of the Lawton IADL was tested by determining its correlation with four scales that measured domains of functional status. All correlations were significant at the .01 or .05 level. The IADL also shows high retest reliability and good inter-rater reliability between personnel from different disciplines [171].
The Functional Independence Measure (FIM) scale (see Appendix 6) assesses physical and cognitive disability. This scale focuses on the burden of care – that is, the level of disability indicating the burden of caring for them [150]. It provides a uniform system of measurement for disability based on the International Classification of Impairment, Disabilities and
Handicaps; measures the level of a patient's disability and indicates how much assistance is required for the individual to carry out activities of daily living [172].
The Northwick Park Index of Independence in ADL (see Appendix 7) is a rating scale that was developed to quantify an individual’s needs for nursing care and support, particularly in highly dependent patients. It has been shown to correlate well with other measures of
36 dependency, including the Barthel Index (BI) and the FIM, It has been shown that it is a reasonably valid and reliable measure of nursing dependency in a rehabilitation unit [173].
The Frenchay Activities Index (FAI) (see Appendix 8) has been developed specifically for measuring disability and handicap in stroke patients. It is a useful stroke-specific instrument to assess functional status. The study in [174] has shown that the reliability of this instrument is sufficient and the construct validity was supported by meaningful correlations between the FAI and scores on the Barthel Index and Sickness Impact profile.
The Nottingham Extended ADL Scale (EADL) (see Appendix 9) is frequently used in clinical practice and research in rehabilitation to assess patients’ independence in activities of daily living [175]. It has been demonstrated that the Nottingham EADL to have adequate validity and reliability for assessing ADL in people with multiple sclerosis, although it may be possible to improve the scale through the addition of items at more extreme ends of the continuum of disability [176].
The PULSES profile (see Appendix 10) is an acronym for 6 functions and factors which are important in the rehabilitation of the disabled individual and can help identify the severity of the disability [177]. The research in [178] concludes that the PULSES profile is a valid measure for assessing disability level in an inpatient stroke rehabilitation setting. The
PULSES profile has a high correlation with the FIM.
The Duke Activity Status Index (see Appendix 11) is a 12-item questionnaire that utilizes self-reported physical work capacity to estimate peak metabolic equivalents (METs) and has been shown to be a valid measurement of functional capacity [179]. It can provide a
37
standardized assessment of functional status that correlates well with an objective measure
of maximal exercise capacity [180].
The Bristol Activities of Daily Living Scale (BADLS) (see Appendix 12) is a 20-item
questionnaire designed to measure the ability of someone with dementia to carry out daily
activities such as dressing, preparing food and using transport [181]. BADLS has been
shown to have face validity, construct validity, concurrent validity and good test-retest
reliability [182].
3.2.2 Behavioral Model of the Elderly
In the medical domain, functional health assessment of the elderly is generally based on the
reviewed measurement instruments. The activities or behaviors in these measurement scales
have been proven to be correlated to one’s health status. Therefore, health related behavioral
model of the elderly has to be defined based on such measurement scales. The outcome will
be a well-defined behavioral model that can serve as a theoretical foundation for related
research. To build the multi-dimensional behavioral model of the elderly, the content of the
measurement scales or instruments used in functional healthcare of the elderly is
summarized in Table 7. Note that non-activity behavioral items are italicized.
Table 7: Content of the measurement scales used in functional healthcare of the elderly Scales Items Barthel continence (bowel and bladder), grooming, toileting, feeding, transfer, mobility, dressing, stairs, bathing Katz continence (bowel and bladder), toileting, feeding, transfer, dressing, bathing Lawton telephone use, shopping, food/tea preparation, housekeeping, laundry, mode of transportation, responsibility for medication, ability to handle finances FIM continence (bowel and bladder), grooming, toileting, feeding, transfer, mobility, dressing, stairs, bathing, expression, comprehension, social interaction, problem solving, memory Northwick continence (bowel and bladder), grooming, toileting, feeding, transfer, mobility, dressing, stairs, bathing, food/tea preparation, taps use Frenchay shopping, food/tea preparation, housekeeping, laundry, mode of transportation, wash dishes, walking out for >15 minutes, actively pursuing hobby, gardening, reading, gainful work Nottingham grooming, feeding, transfer, stairs, telephone use, shopping, food/tea preparation, housekeeping,
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laundry, mode of transportation, ability to handle finances, social interaction, gardening, reading, walking outside, get in/out of cars, walk over uneven ground, corss road, drive a car, write a letter PULSES physical condition, upper limb function, lower limb function, sensory component, excretory function, support function Duke toileting, feeding, transfer, mobility, dressing, stairs, bathing, housekeeping, laundry, gardening, sexual relations, moderate recreational activities, strenuous sports Bristol continence (bowel and bladder), feeding, transfer, mobility, dressing, bathing, telephone use, shopping, food/tea preparation, housekeeping, mode of transportation, ability to handle finances, gardening, drinking, hygiene, teeth clean, orientation-time, orientation-space, communication, games/hobbies
Although the multi-dimensional behavioral model of the elderly doesn’t necessarily have to
cover all aspects related to the health of the elderly, some major ones need to be included,
tested, and validated to guarantee that the dimensions or parameters we are monitoring are
highly related to the health status of the elderly. If so, it can be concluded that the health
status of the elderly has changed if a deviation of monitored data pattern is detected from the
baseline. To evaluate the importance of the activities in Table 7, they have been categorized
into useful, potentially useful, and not useful, regarding how much they are related to the
functional health of the elderly, based on our discussion with the professors in College of
Medicine and College of Nursing at UC. Results shown in Table 8 can serve as the
theoretical multi-dimensional behavioral model of the elderly with solid medical knowledge
support. It would be a valuable tool for other researchers to enable systematic design of
monitoring strategies and techniques that target on improved health outcomes.
Table 8: Theoretical multi-dimensional behavioral model Items Useful Potentially Not Measurement method useful useful Continence (bowel and bladder) x difficult to measure Grooming x hot water usage, bathroom usage Toileting x toilet usage, bathroom usage Feeding x room occupancy, appliance usage Transfer x room occupancy Mobility x room occupancy, activity tracking Weight x weight meter
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Dressing x assign tasks, intervention needed Stairs x activity tracking Bathing x hot water usage, bathroom usage Telephone use x assign tasks, intervention needed Shopping x difficult to measure Food/tea preparation x room occupancy, appliance usage Housekeeping x appliance usage Laundry x room occupancy, appliance usage Mode of transportation x difficult to measure Responsibility for medication x difficult to measure Ability to handle finances x difficult to measure Expression x Comprehension x not activity based Social interaction x not activity based Problem solving x not activity based Memory x not activity based Taps use x assign tasks, intervention needed Wash dishes x Walking out for >15 minutes x assign tasks, intervention needed Actively pursuing hobby x Gardening x Reading x assign tasks, intervention needed Gainful work x Walking outside x assign tasks, intervention needed Get in/out of cars x seat pressure Walk over uneven ground x assign tasks, intervention needed Corss road x assign tasks, intervention needed Drive a car x assign tasks, intervention needed Write letters x Physical condition x difficult to measure, it is general health status Upper limb function x limb movement Lower limb function x limb movement Sensory component x not activity based Excretory function x difficult to measure Support function x difficult to measure Sexual relations x Moderate recreational activities x intervention needed, assign tasks Strenuous sports x Drinking x appliance usage Hygiene x hot water usage, room occupancy Teeth clean x intervention needed, assign tasks
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Orientation-Time x not activity based Orientation-Space x not activity based Communication x not activity based Games/hobbies x
In Table 8, an additional column is added to choose a measurement method for those
activity items that are useful or potentially useful. For some more complicated items such as
shopping, handling finance, etc, they are labeled as ‘difficult to measure’. And some less
complicated ones like telephone use, dressing, etc are labeled as ‘intervention needed, assign
tasks’. What this means is a second person needs to assign a task to see if the monitored
elderly person can complete it without difficulty. Direct sensor measurement can be used to
collect data for some activities such as mobility, laundry, etc. There are also some non-
activity based items, including memory, communication, etc, which are not the focus of this
research. Weight is added into this theoretical multi-dimensional behavioral model because
it is a critical parameter of elderly people’s health, although it is also not an activity based
item.
3.3 Smart Apartment Overview and Sensor Selection for the Smart and Minimum-
intrusive Monitoring System
3.3.1 Smart Apartment Overview
The research is conducted in an apartment in Maple Knoll Community (MKC). The resident
we recruited to participate in experimental trials is an 87 years old woman. A consent form
(see Appendix 2) had been provided to the individual and her family before participating in
this study. The apartment being used has two bedrooms, two bathrooms, living room, dining
41 room, kitchen, and office. The floor plan is shown in Figure 4. Table 9 describes how each room is usually used by the resident.
Figure 4: Floor plan of maple knoll apartment
Table 9: Room usage details of maple knoll apartment Room Usage Master Bath Bathing and toileting Master Bedroom Sleeping Den/Office Watching TV, reading Bath 2 Guest bathing and toileting Bedroom 2 Guest working, playing card games and sleeping Kitchen Cooking, washing dishes Living/Dining Room Eating and talking with people
In addition, the resident usually eats two meals (brunch and dinner) per day. She cooks using microwave more often than using stove. Sometimes she eats dinner in her friends’ apartments. She lives alone most of the time. There’s one caregiver coming every Tuesday and Thursday from 9am to 1pm to help cook her brunch, do some laundry work and clean
42 the room. She has a second caregiver coming to do similar work only when she requests.
She also has a tax assistant coming once a month. Her relatives come twice a year to see her.
She has two cats and there is one person coming once a day to feed the cats and water the plants when she travels out of town. All these could be considered as prior knowledge and would be very useful for our data analysis.
3.3.2 Sensor Selection
As discussed in Section 3.2.2, the theoretical behavioral model could be used as the foundation to select sensors to obtain health related parameters of the elderly. A high-level conceptual schema is illustrated to show how sensors can be mapped to activities in Figure 5.
Those activities that can be measured using sensors are included and linked to sensor functionalities. Other activities related to one’s functional health will be added as we expand our search of relevant literature and medical theory.
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Figure 5: Conceptual schema for sensor mapping
Among the great variety of sensors that are commercially available, we need to identify those that can be used individually or jointly to detect the activities in the behavioral model.
For example, room occupancy sensors alone can detect transferring. As long as the
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monitored subject is alone at home and her presence in different rooms is detected, we can
infer that she is able to move around without caregiver’s assistance. To determine if the
subject is able to use microwave, other than using a room occupancy sensor to detect her
presence in kitchen, we need an appliance usage sensor to detect the operation of the
microwave. Similarly, doing laundry work can be detected with a combination of room
occupancy sensor and appliance usage sensor. Once the links are fully established, the
selection of sensors can be formulated as a constrained cost minimization problem. The need
for constraints is due to the fact that some activities, such as the ability to handle finance and
social activities, etc, are difficult to measure using non-intrusive sensors. In addition,
monitoring cost is also an important issue. If the monitoring system carries a high cost, it is
unlikely to benefit the general elderly population. Based on this, a practical data acquisition
strategy (see Table 10) is established and implemented to install motion sensors in different
rooms for room occupancy monitoring, appliance electrical status sensors for usage pattern
recognition, and activity sensor for walking step, distance, and energy expenditure tracking,
etc. Video cameras will not be used in this research due to their intrusiveness. If cost is not
concerned, other sensors could be selected to measure hot water usage, toilet use, seat
pressure, limb movement and weight. As illustrated in Figure 3, these sensors will be
integrated and synchronized into a wireless sensing network system in which data can be
accessed and visualized in cloud severs.
Table 10: Practical data acquisition strategy
Dimension Data Sensor
Room occupancy Binary data with timestamp indicating if Motion sensor motion is detected
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Household appliance usage Appliance electrical status (on/off) with Smart power meter timestamp (Smappee or Neurio)
(1) Step count with timestamp (2) Walking distance with timestamp Activity and mobility (3) Activity calories burned with Fitbit tracker timestamp (4) Floor climbed with timestamp (5) Active minutes (sedentary, lightly active, fairly active, very active)
After defining the multi-dimensional behavioral model of the elderly, it can be seen in Table
10 that corresponding sensors have been selected for data acquisition. The functions and
characteristics of the selected sensors for each dimension are introduced as follows:
(1) Room occupancy. The Monnit Wi-Fi motion sensor (see Figure 6) is a good option for
room occupancy monitoring because it allows to sense motion, almost always used to detect
whether a human has moved in or out of the sensing range. It is also small, inexpensive,
low-power, easy to use and doesn't wear out [183].
Figure 6: The Monnit Wi-Fi motion sensor
The Monnit Wi-Fi motion sensor uses an infrared sensor to accurately detect movements
made by people/animals within 16.4 ft (5 m) range. An integrated 802.11 b/g radio
46 transmitter allows the device to be easily programmed to work with any existing Wi-Fi network. User defined transmission intervals (heartbeats) and sensor threshold settings ensure that sensor data is received when needed, based on the application. All sensor data is stored securely in the iMonnit Online Sensor Monitoring software and is viewable and exportable from any web enabled device. Email and SMS text messages can be configured through the online system to alert the user immediately when a defined parameter has been met or exceeded [184].
(2) Appliance usage pattern. Household appliance usage pattern sensor can be used to monitor elderly person’s day-to-day activities by tracking the uses of electrical appliances such as refrigerator, microwave oven, television, room heater, and washing machine, etc.
Two options, Neurio [185] and Smappee [186], have been evaluated for appliance power usage monitoring. Basically the two sensors can monitor the power usage in the house.
Comparatively, Smappee is more mature because it can identify the usage (on/off) status of major appliances with timestamp and send the notification to mobile devices, while Neurio is still in its development stage.
Figure 7: Innovative energy monitor: Smappee [186]
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Smappee (see Figure 7) is a device for monitoring the precise energy consumption of household electrical appliances by means of a single sensor. A measuring clamp is attached to the main cable next to the fuse box, so no extra energy meters are required for each individual appliance. There is a free app for iOS or Android available to give an instant overview of the current electricity consumption, standby power consumption and solar panel yield. The stylish graphics show users which appliances are switched on and off, and when.
In addition, Smappee gives users tips and messages about their consumption allowing them to take actions where necessary [186]. The main features of Smappee are:
a) A single overview of total energy consumption, standby power consumption and solar
panel yield.
b) Identification of the electrical appliances in one’s home.
c) Timeline showing when appliances are switched on and off.
d) Graphs showing consumption and costs in real time, per day, month or year.
e) Messages, warnings and consumption tips.
f) Social media tools.
(3) Activity and mobility. As more attention has been paid to help people achieve healthier, more active lives, newly developed activity sensors have become available in the market in recent years. Fitbit Force tracker (see Figure 8) embedded with 3-axis accelerometer and altimeter, as a powerful Force for everyday fitness, motivates us to be more active. It is a high-performance wristband sensor which can track steps taken, distance traveled, floors climbed, active minutes, and calories burned throughout the day, etc. Fitbit Force tracker offers the only trackers that sync wirelessly and automatically in the background through
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Bluetooth 4.0. Force syncs with computers, iPhones and some Android devices [187]. Data synchronized can be visualized in Fitbit Dashboard (see Figure 9).
Figure 8: Fitbit Force tracker [187]
Figure 9: Synchronized data visualization of Fitbit Force tracker
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Figure 10 shows where these sensors have been installed. The installed sensors include six
Monnit wireless motion sensors, one Smappee appliance usage sensor, and one Fitbit activity tracker. Monnit wireless motion sensors are deployed in rooms, such as master bedroom, master bathroom, den/office, kitchen, laundry room, and living/dining room, to monitor room occupancy and motion of the subject. The sensor placement has been tested and optimized based on the guideline [188] for better sensing range and communication.
When motion is detected, one data sample is created with a format consisting of sensor ID, timestamp, and a value of 1 indicating the detected motion. The Smappee appliance usage sensor is installed near the electrical fuse box in the laundry room. It collects data including on/off status of main appliances and power consumption in the apartment with timestamp.
The Fitbit activity tracker is a high-performance wristband sensor worn by the subject.
Embedded with 3-axis accelerometer and altimeter, it tracks steps taken, distance traveled, floors climbed, active minutes, and calories burned throughout the day. All the sensors are cloud-based and the collected data can be retrieved through a web client or iphone app.
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Figure 10: Sensor installation in maple knoll apartment
3.4 Summary
This section presents some concepts and issues in healthcare measurement of the elderly, including health domains of the elderly, commonly used assessment scales, and psychometric properties of health measurement scales. Since functional health of the elderly would be focused on in this research, the relevant measurement scales or instruments in functional healthcare of the elderly are reviewed. To build the multi-dimensional behavioral model of the elderly, the content of the measurement scales or instruments used in functional healthcare of the elderly is summarized. Based on this, a theoretical multi- dimensional behavioral model of the elderly is proposed, which would be a valuable tool for other researchers to enable systematic design of monitoring strategies and techniques that target on improved health outcomes. In addition, measurement method is analyzed for each
51 item in the behavioral model. A high-level conceptual schema is illustrated to show how sensors can be mapped to those behavioral items that can be measured using sensors. A practical data acquisition strategy is established and sensors are selected and installed in one apartment in MKC to collect data of room occupancy, appliance usage and activity tracking.
With all these information, the procedure of designing the smart and minimum-intrusive monitoring framework for health assessment of the elderly is described as follows:
Step 1: Find the smart apartment to conduct the research and establish the blueprint of the overall framework.
Step 2: Initialize the multi-dimensional behavioral model of the elderly and select sensors and data acquisition devices for each dimension.
Step 3: Collect behavioral data and analyze data using intelligent algorithms.
Step 4: Test and evaluate each dimension of the behavioral model on reliability, validity, and responsiveness.
Step 5: If the multi-dimensional elderly behavioral model is validated, finalize and finish; if not, go back to step 2.
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Figure 11: Procedure of designing the smart and minimum-intrusive monitoring framework
The flow chart is shown in Figure 11. So far we have discussed the first two steps. The next section will show the data analytics for the multi-dimensional behavioral model of the elderly.
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4 Data Analytics for the Multi-dimensional Behavioral Model of the Elderly
4.1 Room Occupancy Monitoring
The room occupancy data collection was started on 01/01/2015. Six Monnit wireless motion sensors are deployed in master bedroom, master bathroom, den/office, kitchen, laundry room, and living/dining room to monitor room occupancy and motion of the subject. The sensors are set to have a re-arm time of 1 minute. It means Monnit wireless motion sensor will send only one detected motion data sample to database every minute if there is one subject keeping moving in a room. It will not send any detected motion data sample until the sensor is re-armed in the next minute even if there is another detected motion in the same minute. The communication interval of no motion is configured to be 10 minutes such that the sensor will send one no motion data sample to database every 10 minutes. The collected data can be exported from iMonnit online wireless sensors portal. As shown in Figure 12,
‘Data from all sensors in network’ should be selected in order to have data from all sensors in the exported CSV file (see Figure 13). ‘MessageID’ and ‘SensorID’ (useful to indicate in which room) are automatically checked and we also checked ‘Date’ (timestamp) and ‘Value’
(1: detected motion and 0: no motion). Our data is exported and saved on a daily basis so the
‘Date Range’ is fixed to be one day. In addition, an email notification has been set up for each sensor in the network when the battery state of charge is below 15%.
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Figure 12: iMonnit online wireless sensors portal
Figure 13: Room occupancy data
To streamline and visualize the room occupancy data, a signal processing program is written in MATLAB. Take one day 01/24/2015 when the subject is alone at home for example,
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Figure 14 shows at what time each sensor detects a motion during a day, as well as occupancy percentage pie chart based on the number of detected motion in different rooms.
From the upper figure we can infer that the monitored subject went to bed around 0:30am and got up around 9am. Before going to bed she used master bathroom and went back to master bedroom. After getting up she used master bathroom again and stayed in living/dining room probably having something to drink, then she used den/office, master bedroom, and laundry room. Around 12:00pm she went to bathroom and made her brunch at about 12:30pm in kitchen. She stayed in the living/dining room most of the time in the afternoon and used kitchen around 4:00pm and 6:00pm to make dinner. The master bathroom was used around 10:00pm to take a shower before she went to sleep. Room occupancy pie chart is plotted based on the number of detected motion in different rooms.
The result shows that the monitored subject used kitchen (26%), master bathroom (25%), living/dining room (22%), and master bedroom (19%) more often than laundry room (5%) and den/office (2%) on 01/24/2015.
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Figure 14: Room occupancy data visualization when subject is alone at home
As indicated in Section 3.3.1, there’s one caregiver coming every Tuesday and Thursday from 9am to 1pm to help cook her brunch, do some laundry work and clean the room. The data on 01/13/2015 (Tuesday) when the caregiver comes is visualized in Figure 15.
Compared with Figure 14, the detected motion from 9am to 1pm in Figure 15 is much denser because there were two people moving in the apartment. The subject traveled to
Florida to see her daughter from 01/16/2015 to 01/22/2015, so the data of 01/18/2015 is also plotted in Figure 16 to show how it looks like when she is absent. The data turns out to be very sparse during most of the day except the time around 10pm, which could probably be the time when her friend came to feed the cats and water the plants. And the detected motions when there is nobody in the apartment are from her cats.
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Figure 15: Room occupancy data visualization when caregiver comes
Figure 16: Room occupancy data visualization when subject is absent
Other than plotting the room occupancy data, another function of the MATLAB signal processing program is to extract features characterizing the mobility and transfer of the
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monitored subject. The formulas and explanations of extracted features are listed in Table 11.
These features are saved in a feature set (.mat file) on a daily basis and could be used for
building baseline model in future.
Table 11: Feature extraction for room occupancy data
Feature Formula/Explanation Note
Useful for describing if resident is number_of_ motion Number of detected motion of all sensors during a day. at home and how active he/she is
motion_ratio number__ of motion Useful for describing if resident is motion_ ratio number____ of total data samples at home and how active he/she is
number_of_room_c (1) Sort all data samples of detected motion hange chronologically; Useful for describing how active (2) Compare each sample with the previous sample, the resident is count one room change if sensor IDs are different.
occ_den number____/ of motion in den office Occupancy percentage of occ_ den number____ of total data samples den/office
occ_bathroom number____ of motion in bathroom Occupancy percentage of occ_ bathroom number____ of total data samples bathroom
occ_kitchen number____ of motion in kitchen Occupancy percentage of kitchen occ_ kitchen number____ of total data samples
occ_livingroom number____ of motion in livingroom Occupancy percentage of living occ_ livingroom number____ of total data samples or dining room
occ_bedroom number____ of motion in bedroom Occupancy percentage of occ_ bedroom number____ of total data samples bedroom
occ_laundry number____ of motion in laundry Occupancy percentage of laundry occ_ laundry number____ of total data samples room
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A second MATLAB program can be used to load the saved feature set and visualize some features. For instance, two features, number of motion and number of room change, are plotted from 01/01/2015 to 01/31/2015 in Figure 17. To the best of our knowledge, caregivers came on day 2, 5, 6, 8, 12, 13, 15, 23, 27, 29 in the month of January so that we can see from the figure the values of these two features are greater. In addition, the feature values from 01/16/2015 to 01/22/2015 decreased due to her travel to Florida. Table 12 lists the feature values for the three scenarios (when subject is alone at home, when caregiver comes, when subject is absent) we mentioned above. It is not difficult to see that these two features can reflect the active level of the residents effectively. And they could be even more accurate to characterize the mobility and transfer of the monitored subject if he/she does not have caregivers and pets.
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Figure 17: Room occupancy features in January
Table 12: Room occupancy feature value comparison
Date Scenario Number of motion Number of room change
01/13/2015 when caregiver comes 654 377
01/24/2015 when subject is alone at home 297 100
01/18/2015 when subject is absent 94 65
To summarize room occupancy monitoring, transfer and mobility can be directly measured using the two features described above. The other behaviors of the elderly linked to room occupancy in Figure 5 can only be indirectly measured or detected. For example we are not able to detect food/tea preparation only from room occupancy data. Instead, it can be easily recognized by appliance usage monitoring which will be introduced in next section.
Nonetheless, room occupancy data can provide evidence that the subject is in kitchen when he/she is cooking. The second observation is that PIR motion sensors can hardly make the distinction between the monitored subject and other people/pets nearby. Therefore, it is recommended to recruit those elderly people who live independently most of the time and do not have pets so we can achieve even better accuracy through room occupancy monitoring.
4.2 Appliance Usage Monitoring
The appliance usage data collection was started on 01/04/2015. Smappee sensor is installed in the fuse box of the apartment to monitor elderly person’s day-to-day activities by tracking
61 the uses of electrical appliances. It uses highly innovative software which analyzes the measured current to track what appliances are switched on and off, and when (see Figure 18).
Figure 18: Smappee event list of appliance usage
The first column is the timestamp for each event. The second column provides the information of which appliance has been turned on/off but no more than an index. In order to identify the appliances that we are interested in, a notification function needs to be set up in the iPhone Smappee App for each interested appliance. Then we manually turn on/off different appliances to see which specific appliance usage is notified by the Smappee App.
For example, if appliance 15 is notified right after toaster is turn on/off, we can label appliance 15 to be toaster. The third column indicates power consumption in Watt and on/off status. A positive number means the appliance is turned on, while negative number means it is turned off. Based on the behavior-sensor mapping in Figure 5 and living habits of the monitored subject, six appliances have been identified in Table 13.
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Table 13: Identified appliances using iPhone Smappee App
Index Appliance Appliance Location
7 Lights in fridge Kitchen
11 Iron Laundry room
14 Washing machine Laundry room
15 Toaster Kitchen
17 Microwave Kitchen
21, 28 Stove Kitchen
The data shown in Figure 18 is saved into .csv files on a daily basis. A feature extraction algorithm is programmed in MATLAB to process and visualize the data. Figure 19 illustrates at what time, which appliance was used on 01/13/2015. Iron, washing machine, and stove were used by caregiver between 9am and 1pm. Toaster was used around 7pm probably by the monitored subject for dinner preparation because the caregiver had already left. The door of refrigerator was opened and closed several times during the day for getting food and drink. Microwave was not used on that day so there is no such event plotted in
Figure 19.
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Figure 19: Appliance usage data visualization when caregiver comes
Figure 20: Appliance usage data visualization when subject is alone at home
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Figure 21: Appliance usage data visualization when subject is absent
To better compare the appliance usage data with room occupancy data, the two scenarios when subject is alone at home and when subject is absent are visualized in Figure 20 and
Figure 21, respectively. It is shown that in Figure 20 there is much less appliance usage activities when the subject is alone at home. Besides using refrigerator several times during the day, only toaster was used to cook some bread around 4pm. The stove usage event around 4am would probably be an outlier because the subject was more likely sleeping at that time and there is no motion detected in kitchen around 4am in Figure 14. Even less appliance usage activities are shown in Figure 21, only refrigerator was used around 10pm.
This is probably done by the subject’s friend who comes to feed the cats and water plants when the subject is away from home. Figure 16 also shows the detected motion correspondingly in kitchen around that time, which further proves our inference. Similarly, motion was also detected for each appliance usage activity in the corresponding room if we
65 compare Figure 20 with Figure 14, and Figure 19 with Figure 15 for the other two scenarios.
Therefore, room occupancy sensor and appliance usage sensor can be used jointly to provide convincing evidence for appliance usage activity recognition.
Another MATLAB program is written to view the data from a different angle: for each specific appliance, at what time it is used on different days. The following figures show when refrigerator, iron, washing machine, stove, toaster, and microwave were usually used from 01/04/2015 to 01/31/2015.
Figure 22: Usage pattern of refrigerator
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Figure 23: Usage pattern of iron
Figure 24: Usage pattern of washing machine
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Figure 25: Usage pattern of stove
Figure 26: Usage pattern of toaster
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Figure 27: Usage pattern of microwave
From the figures above it could be observed that the usage of refrigerator is very random at the time whenever the monitored subject takes food or drink. The usage of iron, washing machine, and stove happen more frequently between 9am and 1pm because these appliances are usually used by the caregiver who comes during that time frame. Sometimes stove is also used by the subject to cook dinner in the evening. Toaster is more often used in her brunch time to cook bagels and the usage of microwave is mostly detected in the evening for dinner preparation. With the accumulation of appliance usage data, the living habit of the subject could be better understood, which is beneficial for building the customized behavioral baseline model for the subject.
Another type of data we can export is the weekly power consumption of the entire apartment.
Figure 28 depicts the power consumption from 01/18/2015 to 01/25/2015. The time interval
69 along X-axis is 5 minutes and the unit of Y-axis is Watt. We can see that the power consumption data in the first 4 days has similar pattern because only air conditioner was working when the subject is absent, while the data of the rest of the week becomes less regular as she came back and started to use all kinds of appliances.
Figure 28: Power consumption data from 01/18/2015 to 01/25/2015
Although Smappee is a great sensor that provides appliance usage event and power consumption data, it still has two main flaws. The first is that some appliances may be confused with others that use similar power since Smappee detects appliances based on the pattern of current. Also certain appliances are a combination of a number of electrical components such as e.g. a heating element, a pump, a motor, a light. Certain devices use e.g. the same heating element so that Smappee has difficulties to detect the difference between e.g. a toaster that would have a heating element of 1000W and a dishwasher with also a heating element of 1000W [189]. The second is that when there are a lot of ‘electrical’
70 activities in the home, Smappee may miss an ‘on’ or ‘off’ event. For example, if we see the current status is ‘on’ for appliance 2, the previous status of appliance 2 is expected to be
‘off’. But for some appliances in the list, after the status of ‘on’, there are more than one
‘off’, could be twice, triple or even more ‘off’ consecutively, and there is no ‘on’ status in between. However, Smappee cloud analytics compensate for these missed events so that the overall consumption of the specific appliances is right [190].
Refer back to the behavioral model in Figure 5, food/tea preparation (using cooking appliances), laundry (using washing machine), and housekeeping (using iron) can be directly detected by Smappee, although most of the work is done by her caregiver. Based on this, it could be concluded that these activities are also detectable if the monitored subject lives alone and be able to perform these tasks. Further, we can reasonably infer that one elderly person is able to perform feeding and drinking if he/she can use cooking appliances himself/herself. This requires we have the prior knowledge that he/she lives independently and does not have caregivers coming regularly.
4.3 Activity Tracking
Unlike room occupancy and appliance usage monitoring that require independently lived elderly person for better accuracy, Fitbit Force tracker is used to be worn by the monitored subject for direct measurement of steps taken, distance traveled, floors climbed, active minutes, and calories burned throughout the day, etc. The monitored subject is suggested to put the Fitbit tracker on after getting up in the morning and take it off before going to bed in the evening. The data can be synchronized and visualized in Fitbit Dashboard (see Figure 9).
It is also exportable to .xlsx files on a daily basis. Table 14 lists the included feature and how each one is calculated.
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Table 14: Fitbit Force features
Number Feature Calculation 1 Calories burned Based on BMR (Basal Metabolic Rate) using the height, weight, age, and gender information. 2 Steps Based on built-in accelerometer. 3 Distance Multiplication of steps and estimated stride length based on height. 4 Floors Based on built-in altimeter. 5 Minutes sedentary Metabolic equivalents (METs) around 1. 6 Minutes lightly active Metabolic equivalents (METs) below 3. 7 Minutes fairly active Metabolic equivalents (METs) at or above 3. 8 Minutes very active Metabolic equivalents (METs) at or above 6. 9 Activity calories Based on calorie burn from active minutes.
The activity tracking data collection was started on 01/01/2015. The following figures
illustrate these features from 01/01/2015 to 02/08/2015. From Figure 29 we can see that the
monitored subject usually does not walk more than 2000 steps and moves no more than a
mile every day. Due to the fact that she stays at home most of the time so she usually does
not climb stairs either. Also, the step data and distance data seem to correlate well because
the distance is the multiplication of steps and estimated stride length based on height. In
addition, some step data is 0 or close to 0 on some days because she forgot to wear the Fitbit
tracker on the entire day or only worn it sometime during the day. From the top figure it can
be seen these days are day 11, 23, 24, 25, 26, 30, 31, 32, 33, 37, 38, and 39. If Fitbit tracker
is not worn on the entire day, the burned calories would be estimated to be 1440 based on
the age, sex, height and weight of the monitored subject, as shown in Figure 30. The total
calorie data value on those days when she worn Fitbit tracker is surprisingly to be smaller
than 1440 calories. This reflects that her activity level is below what it is expected to be.
Activity calories shown in the lower figure are mostly below 200 calories, which is
calculated based on her active minutes. Figure 31 shows the minutes data for each activity
level. They are calculated using metabolic equivalents (METs). A MET is a unit that
72 represents the amount of oxygen used by a body during physical activity; therefore, METs are useful indicators for the intensity of physical activities [191]. It is shown that most sedentary minutes are around 1400 and most very active minutes are 0. Light active minutes and fairly active minutes vary a lot based on each day’s activity level. And these values would tend to be 0 if the Fitbit tracker is not worn by the monitored subject.
Figure 29: Fitbit step, distance, and stair data
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Figure 30: Fitbit total calorie and activity calorie data
Figure 31: Fitbit minutes data for different active levels
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In sum, Fitbit tracker can provide direct measurement for mobility and stair included in the behavioral model of the elderly. Measurement in activity tracking does not require the elderly person to live independently because the collected data wouldn’t be interfered by other people nearby. However, it does require the elderly person to remember to wear the
Fitbit tracker as suggested; otherwise the data quality could be affected.
4.4 Summary
In section 4 we have described how the items in the multi-dimensional behavioral model of the elderly are measured with our practical data acquisition strategy. For the behavioral items that can be measured using sensors in Figure 5, Table 15 summarizes which of them can be measured directly and which of them can be measured indirectly in this feasibility study.
Table 15: Behavioral item measurement summary
Item Measurement Transfer Room occupancy Mobility Room occupancy, activity tracking Direct measurement Stairs Activity tracking Food/tea preparation Appliance usage, room occupancy Laundry Appliance usage, room occupancy Housekeeping Appliance usage, room occupancy Grooming Room occupancy Bathing Room occupancy Indirect measurement Hygiene Room occupancy Toileting Room occupancy Feeding Appliance usage, room occupancy Drinking Appliance usage, room occupancy
If we assume that the monitored subject lives alone without caregivers and pets and is able to remember to wear Fitbit tracker as suggested. The collected behavioral data from direct measurement can be accumulated to build her customized baseline model when her health
75 status is good. When her health deteriorates, the collected data could be used to build a recent behavior model. The pattern of these two models should be compared to see if a deviation can be detected.
Figure 32: Data comparison between normal behavior and recent behavior
Take transfer and mobility as an example, two extracted features, number of room change
(x-axis) and number of motion (y-axis), are plotted in Figure 32. The normal behavior pattern that the elderly person usually has around 600 times’ detected motion and 400 times’ room change on each day has been built when she is healthy. But recently it has been observed that both of the two feature values are decreased due to sickness or injury. To quantify the deviation between normal/baseline behavior data and recent behavior data, intelligent algorithm like Gaussian Mixture Model (GMM) [192] can be trained so two distribution mixture models can be estimated for normal behavior (N) and recent behavior
(R) respectively:
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n N x pii h x; i1 m R x qjj h x; j1 where pi or qj is the probability that observation coming from that distribution i or j and h(x;
θ) is distribution probability density function (PDF):
1 1 1 T 1 h x; 1/2 exp x x 22 where is mean vector and is covariance matrix. Then the deviation (L2 distance) between two behavior models can be calculated as:
nm N x R x pi q j h x;; i h x j L2 ij11 L2
Such deviation can be quantified by the behavior assessment tools into a single unit number
H:
N x R x H L2 N x R x LL22
H ranges from 0 to 1. A value close to 1 means the recent behavior pattern is very similar to normal behavior pattern, while a value close to 0 indicates that recent behavior pattern has deviated from normal behavior pattern. It is expected that such deviation can be detected by
GMM if some abnormal behaviors (health decline) of elderly person happened so healthcare professionals can be alerted to provide assistance. Such deviation would be more easily to be detected when elderly patients suffer from acute diseases because the symptoms develop quickly and last a relatively short period of time. For incrementally declined health resulted from chronic diseases, we expect to find the values of some features would change correspondingly with the development of chronic symptoms.
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Similar methods can be applied to other behavioral items in Table 15. Table 16 lists the features we extracted from Section 4.1 to Section 4.3 for each behavioral item. Intelligent health assessment algorithms can be used to evaluate whether the health status of elderly has gone worse. Usually a health index which ranges from 0 to 1 is used to quantify the health status of elderly by analyzing the data pattern similarity between baseline behavioral model and recent behavioral model of the elderly. Logistic regression [193], statistical pattern recognition [194], GMM, and minimum quantization error (MQE) based on self-organizing map (SOM) are useful tools to learn the behavior feature patterns and calculate the health index [195].
Table 16: Useful feature for behavioral items
Item Measurement Feature Transfer Room occupancy Number of motion, number of room change Mobility Room occupancy, Number of motion, number of room change, step, activity tracking distance Stairs Activity tracking Stairs climbed Food/tea preparation Appliance usage, Appliance usage frequency, appliance usage time, room occupancy room occupancy time Laundry Appliance usage, Appliance usage frequency, appliance usage time, room occupancy room occupancy time Housekeeping Appliance usage, Appliance usage frequency, appliance usage time, room occupancy room occupancy time Grooming Room occupancy Room occupancy time Bathing Room occupancy Room occupancy time Hygiene Room occupancy Room occupancy time Toileting Room occupancy Room occupancy time Feeding Appliance usage, Appliance usage frequency, appliance usage time, room occupancy room occupancy time Drinking Appliance usage, Appliance usage frequency, appliance usage time, room occupancy room occupancy time
When we have a larger sample size, the behavioral items in the multi-dimensional behavioral model of the elderly need to be tested and validated to ensure they are reliable, relevant to health of the elderly, and responsive. Due to the nature of our data-driven method,
78 some of the psychometric properties discussed in Section 3.1.2 can be guaranteed. For instance, intra-rater reliability would be high if the same data analysis procedure is applied to the same behavioral item. Responsiveness would be good if our sensors measure accurately and results are updated quickly, etc. The psychometric properties that need to be tested and evaluated are planned as follows:
(1) Reliability. Test-retest reliability needs to be tested for result consistency during a short period of time (assuming there is no significant health deterioration).
(2) Validity. As we are monitoring one single elderly person over time, construct validity should be examined to see if a deviation can be detected when the health of elderly deteriorates. And baseline model pattern and recent behavior model pattern are expected to be similar when elderly person remains healthy. Criterion validity could be considered if there is a gold standard for one specific behavioral item to examine if similar results can be obtained from the gold standard and our method.
After testing the initialized multi-dimensional behavioral model of the elderly, a criterion judging which dimension should be added or removed will be developed based on testing outcomes. Basically those dimensions with high reliability, validity, and responsiveness will be retained in the behavioral model of the elderly. However, some of the retained dimensions are possibly to be correlated. For instance, mobility and stairs have some degrees of correlation because a person with good mobility usually has no difficulty climbing stairs; Doing laundry work is a part of housekeeping so these two dimensions are also related. Therefore, in order to find the optimal set of dimensions that relate to functional health of the elderly, factor analysis [196] needs to be done to simplify the dimension set by
79 extracting independent dimensions and removing redundant dimensions (see Figure 33).
Developing such an intelligent, minimum-intrusive, and low-cost monitoring system for health assessment of the elderly, as a long term goal of this research, requires more tests to find the optimal set of dimensions for behavioral model of the elderly.
Figure 33: Behavioral model dimension conversion using factor analysis
Once the health index of each converted dimension of behavioral model of the elderly is calculated, visualization tools like radar chart [197] can be used to visualize the health indices for all dimensions of the behavioral model. A LabVIEW based Graphical User
Interface (GUI) has been developed in Figure 34. The radar chart GUI has a health radar chart and a risk radar chart which would help healthcare professionals prioritize healthcare decisions. The number of axes of radar charts in this GUI is reconfigurable based on the number of dimensions in the finalized behavioral model of the elderly.
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Figure 34: Health indices visualization for multi-dimensional behavioral model of the elderly
To indicate the overall health status of the elderly, the health indices from all dimensions of the finalized behavioral model of the elderly should be integrated together. For instance, a multi-linear regression model can be used to combine all health indices into a one- dimensional health status indicator:
N T y x = ii x i1 where x = (x1,x2,…, xN) is the N dimensional health index vector, y is the health status indicator, (α, β) = (α, β1, β2, …, βN) is the set of N+1 model parameters. The parameter values should be based on the results of psychometric property testing (reliability, validity, and responsiveness) for the finalized multi-dimensional behavioral model of the elderly as well as the domain knowledge of medical science.
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Figure 35: Trend of health status indicator
With the accumulation of health status indicators for a longer time, its trend can be plotted in
Figure 35. If the health status indicator falls below the pre-established threshold, the healthcare professionals can be notified to give a comprehensive medical diagnosis or change medication plan for the elderly, which is crucial for future treatment and healthcare management. The information can be stored in a knowledge base for tuning the parameters of the multi-linear regression model and health status indicator threshold.
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5 Conclusions
This research aims at designing a systematic framework of the intelligent, minimum- intrusive, and low-cost sensing system for monitoring behaviors of the elderly and assessing their health status. The current development of network-based healthcare monitoring systems of the elderly and the state-of-art on healthcare monitoring technologies including common sensors, behavior modeling strategies, and data analysis methods used in a variety of applications are reviewed. To fill the existing research gaps: (1) lack of a well-defined behavior model or daily activity monitoring strategy; (2) lack of a link between monitored data and health status assessment and inference. The concepts and issues in health measurement of the elderly, such as health domains of the elderly, commonly used assessment scales, and psychometric properties of health measurement scales, are studied.
Further, a multi-dimensional behavioral model of the elderly is developed based on instruments used in functional healthcare of the elderly. Based on this behavioral model, a high-level conceptual schema is illustrated to show how sensors are mapped to those behavioral items that can be measured using sensors. A practical data acquisition strategy is established and sensors are selected and installed in one apartment in MKC to collect data of room occupancy, appliance usage and activity tracking. The collected data are analyzed and visualized. Some features have been extracted to characterize the daily behavioral patterns of the monitored subject. Due to some constraints that we only have one volunteer participating in this feasibility research and her health status is good during the period of data acquisition, it is not the time yet to apply intelligent data algorithms to detect deviation between baseline behavioral model and recent behavior model and conduct psychometric property testing. But we have made a preliminary work plan for these tasks in section 4.4
83 and proposed methods and tools to visualize and integrate the health index of each dimension of behavioral model of the elderly. In addition, it is also suggested to recruit those elderly people who live independently most of the time and do not have caregivers and pets for future studies.
We have demonstrated it is absolutely feasible to assess health status of the elderly using interdisciplinary concepts and tools. Continuous research efforts will be made to validate, improve, and optimize the multi-dimensional behavioral model of the elderly and realize the smart and minimum-intrusive monitoring system for health assessment of the elderly in the long run. The interdisciplinary team is currently working on the idea of establishing an
Advanced Gerontics Engineering (AGE) center and has made preliminary connections with
NSF and a number of potential industrial partners including Procter & Gamble, Hill-Rom, and Samsung Electronics. We will be dedicated to providing considerable benefits to the society by developing technologies to help keep elders at homes while improving the quality of their care and safety.
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Appendix 1: Patent Search Results Using Free Patents Online
Match Patent # Title 1 5410471 Networked health care and monitoring system 2 7808391 Remote caregiver support system 3 8103333 Mesh network monitoring appliance 4 6168563 Remote health monitoring and maintenance system 5 7558622 Mesh network stroke monitoring appliance 6 7539533 Mesh network monitoring appliance 7 8489428 Remote health monitoring and maintenance system 8 7761312 Remote health monitoring and maintenance system 9 5441047 Ambulatory patient health monitoring techniques utilizing interactive visual communication 10 7904310 Blood glucose monitoring system 11 5544649 Ambulatory patient health monitoring techniques utilizing interactive visual communication 12 7853455 Remote health monitoring and maintenance system 13 7877271 Blood glucose monitoring system 14 7624028 Remote health monitoring and maintenance system 15 7941323 Remote health monitoring and maintenance system 16 8260630 Modular microprocessor-based appliance system 17 6757898 Electronic provider—patient interface system 18 6524239 Apparatus for non-intrusively measuring health parameters of a subject and method of use thereof 19 7001334 Apparatus for non-intrusively measuring health parameters of a subject and method of use thereof 20 7269476 Smart medicine container 21 6381577 Multi-user remote health monitoring system 22 6101478 Multi-user remote health monitoring system 23 7970620 Multi-user remote health monitoring system with biometrics support 24 7539532 Cuffless blood pressure monitoring appliance 25 5390238 Health support system 26 8407063 Multi-user remote health monitoring system with biometrics support 27 6334778 Remote psychological diagnosis and monitoring system 28 7107253 Computer architecture and process of patient generation, evolution and simulation for computer based testing system using bayesian networks as a scripting language 29 7277874 Computer architecture and process of patient generation, evolution, and simulation using knowledge base scripting 30 7024399 Computer architecture and process of patient generation, evolution, and simulation for computer based testing system using bayesian networks
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as a scripting language 31 7185282 Interface device for an integrated television-based broadband home health system 32 7835926 Method for conducting a home health session using an integrated television-based broadband home health system 33 7613620 Physician to patient network system for real-time electronic communications and transfer of patient health information 34 8260272 Health-related opportunistic networking 35 8032124 Health-related opportunistic networking 36 7043443 Method and system for matching potential employees and potential employers over a network 37 8285259 Resource aggregation in an opportunistic network 38 7826906 Transducer access point 39 7369919 Medication adherence system 40 8417381 Medication adherence system 41 5995965 System and method for remotely accessing user data records
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Appendix 2: Adult Consent Form for Research
Adult Consent Form for Research
University of Cincinnati
Department: College of Engineering and Applied Sciences, Department of Mechanical and Materials Engineering
Principal Investigator: Dr. Jay Kim
Subject’s Participation in the Elder Care Project
Title of Study: A Smart and Minimum-intrusive Sensing System for Cost-effective Elder Care
Introduction:
You are being asked to take part in a research study. Please read this paper carefully and ask questions about anything that you do not understand.
Who is doing this research study?
The person in charge of this research study (the principal investigator, or PI) is Dr. Jay Kim of the University of Cincinnati (UC) College of Engineering and Applied Sciences, Department of Mechanical and Materials Engineering.
What is the purpose of this research study?
The purpose of this research study is to determine the feasibility of inferring an elder’s health status by monitoring his/her daily activity using a minimum-intrusive sensing system.
Who will be in this research study?
In addition to the PI, the research team includes a registered nurse, a geriatrician, three professors from UC, and a graduate research assistant.
What will you be asked to do in this research study, and how long will it take?
Motion sensors and appliance usage sensors will be installed in your residence. You are also asked to wear a wrist band to measure your activity level and an accelerometer to measure
96 your gait. The study may take as short as a month or as long as three months, depending on when the feasibility of deriving health status from daily behavior inferred from sensor data can be established.
Are there any risks to being in this research study?
Other than the possibility of minimal discomfort such as wearing a wrist band and an accelerometer, there are no real risks involved in this study.
Are there any benefits from being in this research study?
You have the option of requesting a summary of the research outcome, i.e., your daily activity, which may be useful for you to determine whether you keep a healthy daily routine.
Do you have choices about taking part in this research study?
Yes, this is voluntary. If you do not want to take part in this research study you, we will honor your wishes.
How will your research information be kept confidential?
Please note that your information will be kept private and confidential by the research team. Only the research team will handle the research raw data. No personal identifier will be kept. De-identified electronic data will be stored in a dedicated computer in the PI’s lab within the duration of the project and three years after the end of the project. The computer will be password protected and accessible only to the research team. Data will be destroyed using the DataEraser software tool at the end of the three-year holding period after the closure of the project. Agents of the University of Cincinnati may inspect study records for audit or quality assurance purposes.
What are your legal rights in this research study?
Nothing in this consent form waives any legal rights you may have. This consent form also does not release the PI, the institution, or its agents from liability for negligence.
What if you have questions about this research study?
If you have any questions or concerns about this research study, you should contact Dr. Jay Kim at (513) 556-6300. The UC Institutional Review Board reviews all research projects that involve human participants to be sure the rights and welfare of participants are protected. If you have questions about your rights as a participant or complaints about the study, you may contact the UC IRB at (513) 558-5259. Or, you may call the UC Research Compliance Hotline at (800) 889-1547, or write to the IRB, 300 University Hall, ML 0567, 51 Goodman Drive, Cincinnati, OH 45221-0567, or email the IRB office at [email protected].
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Do you HAVE to take part in this research study?
No one is forced to be in this research study. Refusing to take part will NOT cause any penalty or loss of benefits that you would otherwise have. You may initiate being part of the study and then change your mind and stop at any time. To stop being in the study, you should inform Dr. Jay Kim.
Agreement:
I have read this information and have received answers to any questions I asked. I give my consent to participate in this research study. I will receive a copy of this signed and dated consent form to keep.
Participant Name (please print) ______
Participant Signature ______Date ______
Signature of Person Obtaining Consent ______Date ______
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Appendix 3: Barthel Index of Activities of Daily Living [198]
Instructions: Choose the scoring point for the statement that most closely corresponds to the patient's current level of ability for each of the following 10 items. Record actual, not potential, functioning. Information can be obtained from the patient's self-report, from a separate party who is familiar with the patient's abilities (such as a relative), or from observation. Refer to the Guidelines section on the following page for detailed information on scoring and interpretation.
Bowels Transfer 0 = incontinent (or needs to be given enemata) 0 = unable – no sitting balance 1 = occasional accident (once/week) 1 = major help (one or two people, physical), can sit 2 = continent 2 = minor help (verbal or physical) 3 = independent Bladder 0 = incontinent, or catheterized and unable to manage Mobility 1 = occasional accident (max. once per 24 hours) 0 = immobile 2 = continent (for over 7 days) 1 = wheelchair independent, including corners, etc. 2 = walks with help of one person (verbal or physical) Grooming 3 = independent (but may use any aid, e.g., stick) 0 = needs help with personal care 1 = independent face/hair Dressing /teeth/shaving (implements provided) 0 = dependent 1 = needs help, but can do about half unaided Toilet use 2 = independent (including buttons, zips, laces, etc.) 0 = dependent 1 = needs some help, but can do something alone Stairs 2 = independent (on and off, dressing, wiping) 0 = unable 1 = needs help (verbal, physical, carrying aid) Feeding 2 = independent up and down 0 = unable 1 = needs help cutting, spreading butter, etc. Bathing 2 = independent (food provided within reach) 0 = dependent 1 = independent (or in shower)
Scoring: Sum the patient's scores for each item. Total possible scores range from 0 – 20, with lower scores indicating increased disability. If used to measure improvement after rehabilitation, changes of more than two points in the total score reflect a probable genuine change, and change on one item from fully dependent to independent is also likely to be reliable.
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Appendix 4: Katz Index of Independence in Activities of Daily Living [168]
ACTIVITIES INDEPENDENCE (1 Point) DEPENDENCE (0 Points)
Bathes self completely or needs help in Needs help with bathing more than one part BATHING bathing only a single part of the body such of the body, getting in or out of the tub or as back, genital area or disabled extremity. shower. Requires total bathing. Gets clothes from closets and drawers and Needs help with dressing self or needs to be DRESSING puts on clothes and outer garments completely dressed. complete with fasteners. May have help tying shoes. TOILETING Goes to toilet, gets on and off, arranges Needs help transferring to the toilet, clothes, cleans genital area without help. cleaning self or uses bedpan or commode. Moves in and out of bed or chair unassisted. Needs help in moving from bed to chair or TRANSFERRING Mechanical transferring aides are requires a complete transfer. acceptable. CONTINENCE Exercises complete self control over Is partially or totally incontinent of bowel urination and defecation. or bladder. Gets food from plate into mouth without Needs partial or total help with feeding or FEEDING help. Preparation of food may be done by requires parenteral feeding. another person.
TOTAL POINTS = ____ 6 = High (patient independent) 0 = Low (patient very dependent)
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Appendix 5: The Lawton Instrumental Activities of Daily Living Scale [169]
Ability to Use Telephone 1. Operates telephone on own initiative; looks up and dials numbers……1 2. Dials a few well-known numbers……1 3. Answers telephone, but does not dial ……1 4. Does not use telephone at all……0
Shopping 1. Takes care of all shopping needs independently……1 2. Shops independently for small purchases……0 3. Needs to be accompanied on any shopping trip ……0 4. Completely unable to shop……0
Food Preparation 1. Plans, prepares, and serves adequate meals independently……1 2. Prepares adequate meals if supplied with ingredients……0 3. Heats and serves prepared meals or prepares meals but does not maintain adequate diet……0 4. Needs to have meals prepared and served……0
Housekeeping 1. Maintains house alone with occasion assistance (heavy work) ……1 2. Performs light daily tasks such as dishwashing, bed making……1 3. Performs light daily tasks, but cannot maintain acceptable level of cleanliness……1 4. Needs help with all home maintenance tasks……1 5. Does not participate in any housekeeping tasks……0
Laundry 1. Does personal laundry completely……1 2. Launders small items, rinses socks, stockings, etc……1 3. All laundry must be done by others……0
Mode of Transportation 1. Travels independently on public transportation or drives own car……1 2. Arranges own travel via taxi, but does not otherwise use public transportation……1 3. Travels on public transportation when assisted or accompanied by another……1 4. Travel limited to taxi or automobile with assistance of another……0 5. Does not travel at all……0
Responsibility for Own Medications 1. Is responsible for taking medication in correct dosages at correct time……1 2. Takes responsibility if medication is prepared in advance in separate dosage……0 3. Is not capable of dispensing own medication……0
Ability to Handle Finances 1. Manages financial matters independently (budgets, writes checks, pays rent and bills, goes to bank); collects and keeps track of income……1 2. Manages day-to-day purchases, but needs help with banking, major purchases, etc……1 3. Incapable of handling money……0
Scoring: For each category, circle the item description that most closely resembles the client’s highest functional level (either 0 or 1).
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Appendix 6: Functional Independence Measure [199]
Self-care 1. Eating 2. Grooming 3. Bathing/showering 4. Dressing upper body 5. Dressing lower body 6. Toileting
Sphincters 1. Bladder management 2. Bowel management
Mobility 1. Transfers: bed/chair/wheelchair 2. Transfers: toilet 3. Transfers: bathtub/shower 4. Locomotion: walking/wheelchair 5. Locomotion: stairs
Communication 1. Expression 2. Comprehension
Psychosocial 1. Social interaction
Cognition 1. Problem solving 2. Memory
Seven levels for each item: 7. Complete independence: Fully independent 6. Modified independence: Requiring the use of a device but no physical help 5. Supervision: Requiring only standby assistance or verbal prompting or help with set-up 4. Minimal assistance: Requiring incidental hands-on help only (subject performs > 75% of the task) 3. Moderate assistance: Subject still performs 50–75% of the task 2. Maximal assistance: Subject provides less than half of the effort (25–49%) 1. Total assistance: Subject contributes < 25% of the effort or is unable to do the task
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Appendix 7: Northwick Park Index of Independence in ADL [200]
Instructions: The patient is assessed in a room, and is observed undertaking each activity as described. General guidelines are given at the end.
Transfer: bed to chair In and out of bed to a chair beside bed 0 Cannot get out of bed 1 Moves in and out of bed with assistance 2 Independent
Dressing Patient is asked to undress and dress again 0 Unable to put on any clothes 1 Gets fully dressed and undressed with assistance 2 Independent
Bathing: in and out Patient is asked to get in and out of a bath using bath seat and bath board if necessary 0 Cannot get into bath, or needs more than one assistant 1 Gets in and out of bath with assistance 2 Independent
Bathing: washing Patient is asked to stimulate washing of back, arms and feet (either in bath or in front of wash basin) 0 Unable to wash himself 1 Needs assistance with washing 2 Independent
Lavatory Patient is asked to use or mime use of lavatory 0 Cannot use lavatory 1 Goes to use lavatory with assistance for cleaning and/or clothes 2 Independent in reaching lavatory, cleaning and clothes
Continence Enquiry is made of the patient/nursing staff/relatives as to the continence of bowels and bladder 0 Incontinent, or only able to keep control with help 1 Has ‘occasional’ accidents 2 Continent
Grooming: teeth Patient is asked to demonstrate the cleaning of teeth 0 Cannot clean teeth 2 Cleans teeth
Grooming: other Patient is asked to demonstrate brushing and combing hair (women only), shaving (men only) 0 Cannot brush and comb hair/shave 2 Independent brushing and combing of hair/shaving
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Transfer off floor Patient is asked to lie (or is put to lie) on floor. A chair is placed by his side and he is asked to seat himself in his chair by any method he chooses 0 Cannot get off floor without more than one person helping 1 Gets from floor to chair with assistance 2 Independent
Preparation of tea Patient is asked to make a pot of tea. This requires him to collect items from a high and low cupboard and refrigerator and to manipulate cooker. He makes and serves tea using a trolley if necessary 0 Cannot prepare tea 1 Gets ingredients and prepares tea with assistance 2 Independent
Use of taps Patient is asked to turn on and off normal four pronged taps 0 Cannot turn taps 1 Turns specially adapted taps on and off 2 Independent with standard taps
Cooking Patient is asked to fill and lift a kettle on and off a cooker without using adjacent work surface 0 Cannot lift/fill kettle 1 Lifts and fills kettle with assistance 2 Independent
Feeding Patient is asked to cut and eat a piece of food/simulated food, using a knife and fork or adapted cutlery if necessary 0 Cannot feed himself even with assistance 1 Feeds with assistance 2 Independent
Mobility (indoors) Patient is asked to move from assessment room to stairs, negotiating doors, carpet and polished surfaces. Wheelchair bound patients are rated according to their independence 0 Unable to move between rooms 1 Moves between rooms with assistance 2 Independent
Stairs: up Patient is asked to go up and down a flight of stairs (Ten stairs, maximum depth 19 cm, minimum tread 24 cm. The banister rail may be used) 0 Cannot climb stairs 1 Climbs stairs with assistance 2 Independent
Stairs: down Patient is asked to fill and lift a kettle on and off a cooker without using adjacent work surface 0 Cannot descend stairs 1 Descends stairs with assistance 2 Independent
Mobility (outdoors)
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Patient is asked to negotiate a curb, uneven ground and a slope. Wheelchair bound patients are rated according to their independence 0 Cannot move about outdoors even with assistance 1 Moves about outdoors even assistance 2 Independent
General guidelines:
Total Independence – this means the ability to perform an activity without supervision, direction or active personal assistance. A patient who refuses to perform is scored ‘totally dependent’ even though he is deemed able. The patient may use any method or aid to perform the activity.
Partial dependence – the patient can perform the greater part of the activity himself, but needs assistance (verbal or physical), or supervision to complete the activity.
Total dependence – the activity is carried out for the patient or assistance is required from more than one person.
Scoring:
Originally: 1= total independence 2= partial dependence 3= total dependence Range = 17-55 (good – bad)
Recommended (and shown above): 2= total independence 1= partial dependence 0= total dependence Range = 0-34 (bad – good)
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Appendix 8: The Frenchay Activities Index [174]
In the last 3 months how often have you undertaken: 1. Preparing main meals 0 = Never 2. Washing up after meals 1 = Less than once a week 2 = 1-2 times per week 3 = Most days 3. Washing clothes 0 = Never 4. Light housework 1 = 1-2 times in 3 months 5. Heavy housework 2 = 3-12 times in 3 months 6. Local Shopping 3 = At least weekly 7. Social occasions 8. Walking outside for > 15 minutes 9. Actively pursuing hobby 10. Driving car/going on bus
In the last 6 months how often have you undertaken: 11. Travel outings/car rides 0 = Never 1 = 1-2 times in 6 months 2 = 3-12 times in 6 months 3 = At least weekly 12. Gardening 0 = Never 13. Household/care maintenance 1 = light 2 = Moderate 3 = All necessary 14. Reading books 0 = Never 1 = 1 time in 6 months 2 = Less than 1 time in 2 weeks 3 = More than 1 time every 2 weeks 15. Gainful work 0 = Never 1 = Up to 10 hours/week 2 = 10-30 hours/week 3 = Over 30 hours/week
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Appendix 9: Nottingham Extended ADL Scale [201]
The following questions are about everyday activities. Please answer by ticking ONE box for each question. Please record what you have ACTUALLY done in the last few weeks.
DID YOU…… Not at all With help On your own with difficulty On your own 1. Walk around outside? 2. Climbs stairs? 3. Get in and out of a car? 4. Walk over uneven ground? 5. Cross roads? 6. Travel on public transport? 7. Manage to feed yourself? 8. Manage to make yourself a hot water? 9. Take hot drinks from one room to another? 10. Do the washing up? 11. Make yourself a hot snack? 12. Manage your own money when out? 13. Wash small items of clothing? 14. Do your own housework? 15. Do your own shopping? 16. Do a full clothes wash? 17. Read newspapers or books? 18. Use the telephone? 19. Write letters? 20. Go out socially? 21. Manage your own garden? 22. Drive a car?
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Appendix 10: PULSES Profile [177]
Overview: The PULSES profile is an acronym for 6 functions and factors which are important in the rehabilitation of the disabled individual and can help identify the severity of the disability.
Parameters: (1) P: physical condition (general health status) (2) U: upper limb function (self-care drinking eating dressing donning brace or prosthesis washing bathing perineal care) (3) L: lower limb function (mobility transferring to chair/toilet/tub/shower walking climbing stairs propelling wheelchair). (4) S: sensory functions (sight communication by speech and hearing) (5) E: excretory functions (control of bladder and bowel sphincters) (6) S: support factors (psychological emotional family social financial)
Parameter Capacity Points physical condition medical problems sufficiently stable that medical or nursing monitoring 1 is not required more often than 3 month intervals medical or nurse monitoring is needed more often than 3 month intervals 2 but not each week medical problems sufficiently unstable as to require regular medical 3 and/or nursing attention at least weekly medical problems require intensive medical and/or nursing attention at 4 least daily (excluding personal care assistance) upper limb function independent in self-care without impairment of the upper limbs 1 independent in self-care with some impairment of the upper limb 2 dependent on assistance or supervision in self-care with or without 3 impairment of upper limbs dependent totally in self-care with marked impairment of the upper 4 limbs lower limb function independent in mobility without impairment of lower limbs 1 independent in mobility with some impairment in lower limbs (may need 2 a brace or prosthesis) or fully independent in a wheelchair with no significant architectural or environmental barriers dependent on assistance or supervision in mobility with or without 3 impairment of the lower limbs or partly independent in a wheelchair or there are significant architectural or environmental barriers dependent totally in mobility with marked impairment of the lower limbs 4 sensory component independent in communication and vision without impairment 1 independent in communication and vision with some impairment such as 2 mild dysarthria mild aphasia or need for eyeglasses or hearing aid or need for regular eye medications dependent on assistance an interpreter or supervision in communication 3 or vision dependent totally in communication or vision 4 excretory function complete voluntary control of bladder and bowel sphincters 1 control of sphincters allows normal social activities despite urgency or 2 need for catheter appliance suppositories etc. Able to care for needs without assistance
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dependent upon assistance in sphincter management or else has 3 accidents occasionally frequent wetting or soiling from incontinence of bladder or bowel 4 sphincters support function able to fulfill usual roles and perform customary tasks 1 must make some modification in usual roles and performance of 2 customary tasks dependent on assistance supervision encouragement or assistance from a 3 public or private agency dependent on long-term institutional care (chronic hospitalization 4 nursing home etc.) excluding time-limited hospitalization for specific evaluation treatment or active rehabilitation
PULSES score = SUM (points for the 6 factors-functions)
Interpretation: Minimum score: 6 indicating total independence Maximum score: 24 indicating complete dependence A score >= 12 identifies those who are more severely disabled. This corresponds to a Barthel score of <= 60 where A score >= 16 indicates very severe disability.
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Appendix 11: Duke Activity Status Index [202]
The Duke Activity Status Index is a self-administered questionnaire that measures a patient's functional capacity. It can be used to get a rough estimate of a patient's peak oxygen uptake.
1. Take care of yourself by eating, dressing, bathing, toileting? (2.75) 2. Walk indoors such as around the house? (1.75) 3. Walk a block on level ground? (2.75) 4. Climb a flight of stairs or walk up hill? (5.50) 5. Run a short distance? (8.00) 6. Do light housework such as dusting or washing dishes? (2.70) 7. Do moderate housework: vacuuming, sweeping, or carrying groceries? (3.50) 8. Do heavy housework such as scrubbing floors or moving furniture? (8.0) 9. Do yard work such as raking, weeding, or pushing a mower? (4.5) 10. Have sexual relations? (5.25) 11. Moderate Exercise such as golf, bowl, dance, doubles tennis, throw ball? (6) 12. Strenuous Exercise such as swim, ski, singles tennis, football, basketball? (7.5)
Duke Activity Status Index (DASI) = sum of points VO2peak = (0.43 x DASI) + 9.6 VO2peak = ______ml/kg/min ÷ 3.5 ml/kg/min = ______METS
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Appendix 12: Bristol Activities of Daily Living Scale [182]
This questionnaire is designed to reveal the everyday ability of people who have memory difficulties of one form or another. For each activity (No. 1 - 20), statements a - e refer to a different level of ability. Thinking of the last 2 weeks, tick the box that represents your relative’s friend’s AVERAGE ability. (If in doubt about which box to tick, choose the level of ability which represents their average performance over the last 2 Weeks. Tick ‘Not applicable’ if your relative never did that activity when they were well).
1. PREPARING FOOD a) Selects and prepares food as required b) Able to prepare food if ingredients set out c) Can prepare food if prompted step by step d) Unable to prepare food even with prompting and supervision e) Not applicable 2. EATING a) Eats appropriately using correct cutlery b) Eats appropriately if food made manageable and /or uses spoon c) Uses fingers to eat food d) Needs to be fed e) Not applicable 3. PREPARING DRINK a) Selects and prepares drinks as required b) Can prepare drinks if ingredients left available c) Can prepare drinks if prompted step by step d) Unable to make a drink even with prompting and supervision e) Not applicable 4. DRINKING a) Drinks appropriately b) Drinks appropriately with aids, beaker/straw etc. c) Does not drink appropriately even with aids but attempts to d) Has to have drinks administered (fed) e) Not applicable 5. DRESSING a) Selects appropriate clothing and dresses self b) Puts clothes on in wrong order and /or back to front and/or dirty clothing c) Unable to dress self but moves limbs to assist d) Unable to assist and requires total dressing e) Not applicable 6. HYGIENE a) Washes regularly and independently b) Can wash self if given soap, flannel, towel, etc. c) Can wash self if prompted and supervised d) Unable to wash self and needs full assistance e) Not applicable 7. TEETH a) Cleans own teeth/dentures regularly and independently b) Cleans teeth/dentures if given appropriate items c) Requires some assistance, toothpaste on brush, brush to mouth etc d) Full assistance given e) Not applicable 8. BATH/SHOWER a) Bathes regularly and independently b) Needs bath to be drawn/shower turned on but washes independently c) Needs supervision and prompting to wash d) Totally dependent, needs full assistance e) Not applicable 9. TOILET/COMMODE a) Uses toilet appropriately when required b) Needs to be taken to the toilet and given assistance c) Incontinent of urine or faeces d) Incontinent of urine and faeces e) Not applicable
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10. TRANSFERS a) Can get in/out of chair unaided b) Can get into a chair but needs help to get out c) Needs help getting in and out of a chair d) Totally dependent on being put into and lifted from chair e) Not applicable 11. MOBILITY a) Walks independently b) Walks with assistance i.e. furniture, arm for support c) Uses aids to mobilize i.e. frame, sticks etc. d) Unable to walk e) Not applicable 12. ORIENTATION-TIME a) Fully orientated to time/day/date etc. b) Unaware of time/day etc but seems unconcerned c) Repeatedly asks the time/day/date d) Mixes up night and day e) Not applicable 13. ORIENTATION-SPACE a) Fully orientated to surroundings b) Orientated to familiar surroundings only c) Gets lost in home, needs reminding where bathroom is, etc. d) Does not recognize home as own and attempts to leave e) Not applicable 14. COMMUNICATION a) Able to hold appropriate conversation b) Shows understanding and attempts to respond verbally with gestures c) Can make self understood but difficulty understanding others d) Does not respond to, or communicate with others e) Not applicable 15. TELEPHONE a) Uses telephone appropriately, including obtaining correct number b) Uses telephone if number given verbally/visually or predialled c) Answers telephone but does not make calls d) Unable/unwilling to use telephone at all e) Not applicable 16. HOUSEWORK/GARDENING a) Able to do housework/gardening to previous standard b) Able to do housework/gardening but not to previous standard c) Limited participation with a lot of supervision d) Unwilling/unable to participate in previous activities e) Not applicable 17. SHOPPING a) Shops to previous standard b) Only able to shop for 1 or 2 items with or without a list c) Unable to shop alone, but participates when accompanied d) Unable to participate in shopping even when accompanied e) Not applicable 18. FINANCES a) Responsible for own finances at previous level b) Unable to write cheque. Can sign name & recognizes money values c) Can sign name but unable to recognize money values d) Unable to sign name or recognize money values e) Not applicable 19. GAMES/HOBBIES a) Participates in pastimes/activities to previous standard b) Participates but needs instruction/supervision c) Reluctant to join in, very slow needs coaxing d) No longer able or willing to join in e) Not applicable 20. TRANSPORT a) Able to drive, cycle or use public transport independently b) Unable to drive but uses public transport or bike etc. c) Unable to use public transport alone d) Unable/unwilling to use transport even when accompanied e) Not applicable
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Publications:
1. Chuan Jiang and Samuel H. Huang, “A Computationally Efficient and Adaptive Approach for Online Embedded Machinery Diagnosis in Harsh Environments,” Advances in Mechanical Engineering, vol. 2013, Article ID 847612, 12 pages, 2013. doi:10.1155/2013/847612
2. Chuan Jiang and Samuel H. Huang, “Patent Review on Network-based Elderly Healthcare System in the US,” Recent Patents on Computer Science, 2013, Vol. 6, No. 3
3. Rezvanizanian, Seyed Mohammad, Yixiang Huang, Chuan Jiang, and Jay Lee. "A Mobility Performance Assessment on Plug-in EV Battery," International Journal of Prognostics and Health Management, ISSN 2153-2648, 2012 011
4. Samuel H. Huang, Chuan Jiang, Evelyn Fitzwater, and Qiang Su. “An Overview of Elderly Care Supply Chain and Assistive Technologies,” The 7th International Conference on Operations and Supply Chain Management, June 22-25, Shanghai, P. R. China
5. Jay Lee, Yixiang Huang, and Chuan Jiang. "Rapid Battery State of Charge Measurement and Estimation," U.S. Provisional Patent Application Serial No. 61/811,395
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