DOCSLIB.ORG

Remote Patient Monitoring Using Health Bands With

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Remote Patient Monitoring Using Health Bands With REMOTE PATIENT MONITORING USING HEALTH BANDS WITH ACTIVITY LEVEL PRESCRIPTION by PRANAY SHIROLKAR Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN COMPUTER SCIENCE AND ENGINEERING THE UNIVERSITY OF TEXAS AT ARLINGTON December 2016 Copyright © by Pranay Shirolkar 2016 All Rights Reserved ii Acknowledgements This thesis is a blend of motivation and insights from Prof. David Levine, my Guru, mentor and supervisor. I would like to thank Prof. David Levine with all my heart for encouraging me, motivating me and tracking my progress throughout the work that was being done. It would not be possible without your belief in me and tons of anecdotes from your experience. Thank you again for enlightening me through the path of my career and all the wisdom that I have gained under your supervision. I would like to thank all my committee members, for their patience, faith and confidence in me. I thank Dr. Khosrow Behbehani for his fruitful insights and guidance for the fruition of this project and systematically guiding the way and motivation. I thank Dr. Christoph Csallner to provide me insights on the aspects of software engineering, motivating to pursue something that I believe in and identifying the potential in me. I thank my parents, Mr. Girish Shirolkar and Mrs. Sumati Shirolkar, without their constant care, hope, faith, motivation, and confidence in me I cannot imagine the completion of this thesis. I thank Purva Sugandhi for always being there and having a very patient and perseverant effort at listening and constantly motivating me towards achieving my goals. I thank all the people, my friends, family, relatives, classmates, colleagues, acquaintances whom I have not included here specifically, but they have played an important role in the pursuit of my higher studies and goals. November 3, 2016 iii Abstract REMOTE PATIENT MONITORING USING HEALTH BANDS WITH ACTIVITY LEVEL PRESCRIPTION Pranay Shirolkar, MS The University of Texas at Arlington, 2016 Supervising Professor: David Levine With the advent of new commercially available consumer grade fitness and health devices, it is now possible and very common for users to obtain, store, share and learn about some of their important physiological metrics such as steps taken, heart rate, quality of sleep and skin temperature. For devices with this wearable technology, it is common to find these sensors embedded in a smart watch, or dedicated wearable wrist bands such that among other functionalities of a wearable device, it is capable of smartly assisting users about their activity levels by leveraging the fact that these devices can be, and are typically, worn by people for prolonged periods of time. This new connected wearable technology has a great potential for physicians to be able to monitor and regulate their patients’ activity levels. There exist many software applications and complex Wireless Body Area Network (WBAN) based solutions for remote patient monitoring but what has been lacking is a solution for physicians, especially exercise physiologists, to automate and convey appropriate training levels and feedback in a usable manner. This work proposes a software framework that enables users to know their prescribed level of exercise intensity level and then record their exercise session and securely transmit it wirelessly to a centralized data-store where physiologists will have access to it. iv Table of Contents Acknowledgements ............................................................................................................. iii Abstract ............................................................................................................................... iv List of Illustrations ............................................................................................................. viii Chapter 1 Introduction ......................................................................................................... 1 Background ..................................................................................................................... 2 Related Work ................................................................................................................... 4 Motivation ........................................................................................................................ 7 Goals of the thesis ........................................................................................................... 9 Organization of the thesis.............................................................................................. 10 Chapter 2 Means and Methods ......................................................................................... 12 Health Devices Available & Sensor Capabilities ........................................................... 12 Sensors ..................................................................................................................... 12 Accelerometer ....................................................................................................... 12 Gyrometer ............................................................................................................. 13 Photo-plethysmography Sensor (Optical Heart Rate Monitor) ............................. 13 Optical Thermometer ............................................................................................ 13 Global Positioning System Sensor (GPS) ............................................................ 13 Ambient Light Sensor ........................................................................................... 13 Electro-dermal Activity or Galvanic Skin Response Sensor ................................. 14 Ultra Violet Light Sensor ....................................................................................... 14 Microphone ........................................................................................................... 14 Barometer ............................................................................................................. 14 Research Grade Health Devices .............................................................................. 14 Empatica E4 Band ................................................................................................ 14 v Consumer Grade Health Devices ............................................................................. 15 Microsoft Band ...................................................................................................... 15 Polar H7 Chest Strap Band .................................................................................. 15 Apple Watch.......................................................................................................... 16 Polar A360 ............................................................................................................ 16 Polar M600............................................................................................................ 16 Fitbit Charge 2 ...................................................................................................... 17 Health Metrics................................................................................................................ 17 Heart Rate ................................................................................................................. 17 Steps and Running .................................................................................................... 18 Calorie Expenditure ................................................................................................... 18 Sleep Quality Tracking .............................................................................................. 18 Exercise Session Tracking ........................................................................................ 19 Validation of Accuracy and Reliability of the Devices Used .......................................... 19 Proposed Software System Solution ............................................................................. 22 Architecture ............................................................................................................... 26 Technologies Used.................................................................................................... 27 Data Privacy and Encryption ..................................................................................... 31 Implementation .......................................................................................................... 35 The Hub ................................................................................................................ 35 Patient Mobile App ................................................................................................ 39 Physiologist Web Application ............................................................................... 40 Limitations ................................................................................................................. 42 Chapter 3 Result ................................................................................................................ 45 Functional Overview ...................................................................................................... 45 vi User Interfaces (UI) ......................................................................................................
Load more

Recommended publications
  • Wearable Woes: an Emerging Issue in Product Liability Litigation By
    Wearable Woes: An Emerging Issue in Product Liability Litigation By Sarah Hansen Burden, Hafner & Hansen, LLC 605 Brisbane Building 403 Main Street Buffalo, New York 14203 Popular wearables such as the Fitbit have experienced setbacks as they sell millions of their fitness trackers to the public. These problems have ranged from skin irritation to a failure to accurately monitor the user’s heartrate. Fitbit has fended off multiple lawsuits, including a shareholder lawsuit resulting from alleged failures of their device to work as advertised, resulting in a plunge in its stock value. In October 2013, Fitbit released its Force model and by the end of that year, users were reporting skin irritations on their wrists where they were wearing the fitness tracker.1 The symptoms reported by users ranged from red patches to blisters.2 Apple Watch users also reported skin irritation in the area where they were wearing their watch.3 Several customers who alleged that they suffered from skin irritation commenced a lawsuit against Fitbit claiming to have been misled by their advertising of the device.4 Overall, approximately 1.7% of the Fitbit Force users reported skin irritation.5 As a result of the consumer complaints, Fitbit issued a recall of the Force model offering a full refund.6 Fitbit conducted the recall of the Force with the Consumer Product Safety Commission.7 The recall was announced on February 20, 2014 and involves approximately 1,000,000 units in the United States and another 28,000 in Canada.8 In a letter dated October 17, 2014 from the CEO of Fitbit, James Park, Fitbit announced that after their investigation, it is believed that some users were reacting to the nickel in the stainless steel or to the methacrylate used in the adhesives to manufacture the Force.9 Mr.
    [Show full text]
  • Predicting Student's Attributes from Their Physiological Response to An
    ISSN 2007-9737 Predicción de atributos de estudiantes a partir de su respuesta fisiológica a cursos en línea Marco A. Hernández Pérez1, Emmanuel Rosado Martínez2, Rolando Menchaca Méndez1, Ricardo Menchaca Méndez1, Mario E. Rivero Ángeles1, Víctor M. González3 1 Centro de Investigación en Computación, Instituto Politécnico Nacional, México 2 Escuela Superior de Cómputo, Instituto Politécnico Nacional, México [email protected], [email protected], {rmen, ric, erivero}@cic.ipn.mx, [email protected] Resumen. En este trabajo se presentan los resultados Predicting Student’s Attributes from de un estudio donde se monitorizó la respuesta fisiológica de un conjunto de cincuenta estudiantes de their Physiological Response to an nivel medio superior, durante su participación en un Online Course curso en línea. Por cada uno de los sujetos de prueba, se recolectaron series de tiempo obtenidas por medio de Abstract. In this work, we present the results of a study sensores de señales fisiológicas como actividad where we monitored the physiological response of a set eléctrica cerebral, ritmo cardiaco, respuesta galvánica of fifty high-school students during their participation in de la piel, temperatura corporal, entre otros. A partir de an online course. For each of the subjects, we los primeros cuatro momentos estadísticos (media, recollected time-series obtained from sensors of varianza, asimetría y curtosis) de dichas series de physiological signals such as electrical cerebral activity, tiempo, se entrenaron modelos de redes neuronales y heart rate, galvanic skin response, body temperature, máquinas de vector de soporte que demostraron ser among others. From the first four moments (mean, efectivas para determinar el sexo del sujeto de prueba, variance, skewness and kurtosis) of the time-series we el tipo de actividad que se encuentra realizando, su trained Artificial Neural Network and Support Vector estilo de aprendizaje, así como si contaban o no con Machine models that showed to be effective for conocimientos previos acerca del contenido del curso.
    [Show full text]
  • Healthchampion Connected Apps and Devices 02.06.2020
    HealthChampion Connected Apps and Devices 02.06.2020 Source Device Type Summaries Workouts Measurements Sleep Nutrition FDA Status AsthmaMD AsthmaMD Peak Flow Meter - - Supported - - - BodiMetrics BodiMetrics Performance Monitor Health Monitor Supported - Supported - - - Daily Burn Daily Burn App Activity Tracker - Supported - - - - Dario Blood Glucose Monitoring Dario Glucometer - - Supported - - - System 510(k) Dexcom Dexcom G4 Platinum CGM Glucometer - - Supported - - Cleared 510(k) Dexcom Dexcom G5 CGM Glucometer - - Supported - - Cleared 510(k) Dexcom Dexcom G6 CGM Glucometer - - Supported - - Cleared Coming Emfit Emfit QS Sleep Tracker - - - - - soon Fitbit Fitbit App Activity Tracker Supported Supported Supported Supported Supported - Fitbit Fitbit Aria Scale - - Supported - - - Fitbit Fitbit Aria 2 Scale - - Supported - - - Fitbit Fitbit Aria Air Scale - - Supported - - - Fitbit Fitbit Flex Activity Tracker Supported - - Supported - - Fitbit Fitbit Charge Activity Tracker Supported Supported - Supported - - Fitbit Fitbit Charge HR Activity Tracker Supported Supported - Supported - - Fitbit Fitbit Force Activity Tracker Supported Supported - Supported - - Fitbit Fitbit One Activity Tracker Supported Supported - Supported - - Fitbit Fitbit Surge Activity Tracker Supported Supported - Supported - - Fitbit Fitbit Ultra Activity Tracker Supported Supported - Supported - - Fitbit Fitbit Zip Activity Tracker Supported - - - Fitbit Fitbit Blaze Activity Tracker Supported Supported - Supported - - Fitbit Fitbit Alta Activity Tracker Supported
    [Show full text]
  • Measuring Heart Rate Variability in Free-Living Conditions Using Consumer-Grade Photoplethysmography: Validation Study
    JMIR BIOMEDICAL ENGINEERING Lam et al Original Paper Measuring Heart Rate Variability in Free-Living Conditions Using Consumer-Grade Photoplethysmography: Validation Study Emily Lam1*, BEng; Shahrose Aratia2*, BSc; Julian Wang3, MD; James Tung4, PhD 1Possibility Engineering and Research Laboratory, Bloorview Research Institute, Toronto, ON, Canada 2Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada 3Michael G Degroote School of Medicine, McMaster University, Hamilton, ON, Canada 4Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada *these authors contributed equally Corresponding Author: James Tung, PhD Department of Mechanical & Mechatronics Engineering University of Waterloo 200 University Ave W Waterloo, ON, N2L 3G1 Canada Phone: 1 519 888 4567 ext 43445 Email: [email protected] Abstract Background: Heart rate variability (HRV) is used to assess cardiac health and autonomic nervous system capabilities. With the growing popularity of commercially available wearable technologies, the opportunity to unobtrusively measure HRV via photoplethysmography (PPG) is an attractive alternative to electrocardiogram (ECG), which serves as the gold standard. PPG measures blood flow within the vasculature using color intensity. However, PPG does not directly measure HRV; it measures pulse rate variability (PRV). Previous studies comparing consumer-grade PRV with HRV have demonstrated mixed results in short durations of activity under controlled conditions. Further research is required to determine the efficacy of PRV to estimate HRV under free-living conditions. Objective: This study aims to compare PRV estimates obtained from a consumer-grade PPG sensor with HRV measurements from a portable ECG during unsupervised free-living conditions, including sleep, and examine factors influencing estimation, including measurement conditions and simple editing methods to limit motion artifacts.
    [Show full text]
  • A Mobile Application for Data Acquisition from Wearable Devices in Affective Computing Experiments∗
    BandReader – A Mobile Application for Data Acquisition from Wearable Devices in Affective Computing Experiments∗ Krzysztof Kutt AGH University of Science and Technology [email protected] Grzegorz J. Nalepa AGH University of Science and Technology [email protected] Barbara Gizycka˙ AGH University of Science and Technology [email protected] Paweł Jemio{o AGH University of Science and Technology [email protected] Marcin Adamczyk AGH University of Science and Technology Abstract As technology becomes more ubiquitous and pervasive, special attention should be given to human-computer interaction, especially to the aspect related to the emotional states of the user. However, this approach assumes very specific mode of data collection and storage. This data is used in the affective computing experiments for human emotion recognition. In the paper we describe a new software solution for mobile devices that allows for data acquisition from wristbands. The application reads physiological signals from wristbands and supports multiple recent devices. In our work we focus on the Heart Rate (HR) and Galvanic Skin Response (GSR) readings. The recorded data is conveniently stored in CSV files, ready for further interpretation. We provide the evaluation of our application with several experiments. The results indicate that the BandReader is a reliable software for data acquisition in affective computing scenarios. I. IntroductionDRAFTeach user, mobile systems offer various meth- ods of personalization and customization of eople nowadays are getting more and these devices. However, as they become more more accustomed to technology that is and more miniature, they can be fit to pieces Ppervasive and ubiquitous. Besides using of hardware of a size of an accessory.
    [Show full text]
  • Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study
    JMIR MHEALTH AND UHEALTH Johnson et al Original Paper Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study Amanda Johnson1*, MD, BA; Fan Yang2*; Siddharth Gollarahalli3; Tanvi Banerjee2, PhD; Daniel Abrams4, PhD; Jude Jonassaint5, RN; Charles Jonassaint5, PhD, MHS; Nirmish Shah6, MD 1Department of Pediatrics, Duke University, Durham, NC, United States 2Department of Computer Science & Engineering, Wright State University, Dayton, OH, United States 3North Carolina State University, Raleigh, NC, United States 4Engineering Sciences and Applied Mathematics, Northwestern University, Chicago, IL, United States 5Social Work and Clinical and Translational Science, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States 6Division of Hematology, Department of Medicine, Duke University, Durham, NC, United States *these authors contributed equally Corresponding Author: Amanda Johnson, MD, BA Department of Pediatrics Duke University 2301 Erwin Road Durham, NC, 27710 United States Phone: 1 651 207 3255 Email: [email protected] Abstract Background: Sickle cell disease (SCD) is an inherited red blood cell disorder affecting millions worldwide, and it results in many potential medical complications throughout the life course. The hallmark of SCD is pain. Many patients experience daily chronic pain as well as intermittent, unpredictable acute vaso-occlusive painful episodes called pain crises. These pain crises often require acute medical care through the day hospital or emergency department. Following presentation, a number of these patients are subsequently admitted with continued efforts of treatment focused on palliative pain control and hydration for management. Mitigating pain crises is challenging for both the patients and their providers, given the perceived unpredictability and subjective nature of pain.
    [Show full text]
  • Microsoft Band SDK
    Microsoft Band SDK SDK Documentation By using this Microsoft Band SDK, you agree to be bound by the Terms of Use. Further, if accepting on behalf of a company, you represent that you’re authorized to act on your company’s behalf. 1 Introduction and Features ............................................................................................................ 4 1.1 Multi-Platform Support ..................................................................................................................... 4 1.2 Getting Sensor Data ........................................................................................................................... 4 1.3 Creating Tiles and Sending Notifications ............................................................................................. 6 1.3.1 App Tiles ................................................................................................................................................................... 6 1.3.2 App Notifications ...................................................................................................................................................... 7 1.3.3 Haptic Notifications .................................................................................................................................................. 7 1.3.4 Band Personalization ................................................................................................................................................ 8 1.3.4.1 Me Tile ............................................................................................................................................................
    [Show full text]
  • Microsoft Band 2 Experience Design Guidelines
    1 Microsoft Band 2 Experience Design Guidelines Published for Third Party Version 2.0 Microsoft Band 2 2 Welcome Microsoft Band 2 Experience Design Guidelines These guidelines provide an overview of the Band, and define guidelines for the user experience for developers of third party apps. Microsoft Band 2 3 Table of Contents Introduction Glossary Interaction Patterns Typography Color Iconography Template Library Microsoft Band 2 Introduction 4 Microsoft Band 2 Direct, yet discrete. Forgiving. Experience Principals Notifications are timely, not disruptive and quick to Interactions are hyper-mobile. A nondestructive dismiss. Information passes from you to the cloud and interface tailored for the range of human motion. The experience principles should be back again in a direct, yet personal way. Positive reactions to your actions. Compensates taken into consideration when for error. creating app tiles for the Microsoft Band. Hyper-glanceable. In and out in eight. Buzz, ping, glance, swipe... Indoors or out, dark or Never break the stride of life... Glance, peek, and bright, the information most important to you is decide all in less than eight seconds — the right always at arm’s length. type and amount of information at the right time. Not a mini-phone. About you. Free your hands from the fear of missing out. Provide Knows your patterns and what you’ve done. enough information in the moment so you know if Anticipates your needs through smart and when you need to dig into your phone. It’s all recommendations and tracking. about the right amount of information, so you’ll never miss the moments that are most important to you.
    [Show full text]
  • C-10 Thematic Poster ‑ Bioenergetics and Training
    Official Journal of the American College of Sports Medicine Vol. 49 No. 5 Supplement S247 C-10 Thematic Poster ‑ Bioenergetics and Training RESULTS: Skeletal muscle CS maximal activity increased (P<0.05) from 25.5±3.1 to -1 -1 30.0±3.1 µmol·g ·min in HIT only, with larger (P<0.05) improvement compared to Thursday, June 1, 2017, 8:00 AM ‑ 10:00 AM MIT. Muscle HAD maximal activity increased (P<0.05) in HIT 15.3±1.9 to 18.5±4.0 -1 -1 -1 -1 Room: 403 µmol·g ·min and in MIT (15.7±2.8 to 19.5±3.0 µmol·g ·min ) with no between- group difference. YYIR2 score was improved (P<0.05) ~39 % more in HIT compared 1227 Chair: Gretchen A. Casazza. UC Davis Sports Medicine, to MIT post-intervention (323 ± 125 vs. 222 ± 113 m). Sacramento, CA. CONCLUSIONS: Additional high-intensity and moderate-intensity training augmented skeletal muscle oxidative capacity and high-intensity exercise performance (No relationships reported) in trained athletes with an overall higher effect of high-intensity training. 1228 Board #1 June 1 8:00 AM ‑ 10:00 AM 1230 Board #3 June 1 8:00 AM ‑ 10:00 AM Resistance Exercise Attenuates Mitochondrial Decreased Energy Availability During Intensified Function: Effects Of NSAID Intake And Eccentric- Training is Associated with Non-Functional Overload Training Overreaching in Female Runners Daniele Cardinale1, Mats Lilja2, Mirko Mandic2, Thomas Karine Schaal1, Marta Van Loan, FACSM1, Christophe Gustafsson2, Filip J. Larsen1, Tommy R. Lundberg2. 1The Hausswirth2, Gretchen Casazza1. 1UC Davis, Davis, CA.
    [Show full text]
  • Apple Watch Asus Zenwatch 2 Garmin Vivoactive
    Garmin Motorola Asus Martian Microsoft Band Samsung Sony Smart Apple Watch Vivoactive Huwawei LG Urbane Moto 360 Pebble ZenWatch 2 Passport 2 Gear S2 Watch 3 Smartwatch (2nd gen) $349 - $17,000 $129 - $199 $219.99 $349.99 - $249.99 $299.00 $249.99 $299 - $399 $99 - $250 $299 - $350 $249+ Price $799 • Android tablets • Android • Android • iPhone 5 • Android • Android • Android • Android • Android • Android Compatibility smartphones • iOS Phone Android Android • iPhone 6 • iOS Phone • iOS Phone • iOS Phone • iOS Phone • iOS Phone • iOS Phone • iOS Phone • Windows • iPad (Air, Mini, 3rd Gen) • Bluetooth • Bluetooth • Bluetooth • Bluetooth • Wi-Fi • Bluetooth • Bluetooth • Bluetooth • Wi-Fi • Wi-Fi • Wi-Fi • NFC • Wi-Fi • Wireless • Wireless • Wireless • Bluetooth • Bluetooth • Wireless • Wireless technology Connectivity • Bluetooth • Wireless • Bluetooth Synching Syncing Syncing • Wi-Fi • Wi-Fi Syncing Syncing Syncing • Automatic • Automatic • Automatic • Automatic • NFC Synching Syncing Syncing Syncing Technology 2-7 days Battery Life Up to 18 hrs Up to 2 days Up to 3 weeks Up to 2 days 410mAh battery 5-7 days 48 hours Up to 2 days (depending on 3+ days 2-3 days model) • 38mm • 1.45" • 42mm Screen Size 1.13" 1.4" 1.3" 1.01" 31mm 32mm 1.63" 1.6" • 42mm • 1.63" • 46mm Retina Display AMOLED LCD AMOLED P-OLED OLED AMOLED Digital E-paper AMOLED LCD Display 8GB Total 4GB 4MB 4GB 4GB N/A N/A 4GB Limited 4GB 4GB Memory Storage Yes Yes Yes Yes Yes Splash proof No Yes Yes Yes Yes Water Resistance • Accelerometer • • Accelerometer • Gyroscope • Heart rate • Accelerometer • Heart rate • • • • Accelerometer • Gyroscope Accelerometer • Heart rate • Acceleromet • Accelerometer Accelerometer Accelerometer Accelerometer • Gyroscope • Heart Rate • Gyroscope monitor er • Gyroscope • Multi sport • S Health and • Compass • Built-in • Swimming • Barometer • Ambient light tracking Nike+ Running pedometer • Golfing • Heart rate sensor integration to Health Tracker • ZenWatch • Activity • Skin temp track health Wellness App tracking sensor and fitness.
    [Show full text]
  • An Adaptive Learning System Based on Real-Time User Emotions
    SSStttooonnnyyy BBBrrrooooookkk UUUnnniiivvveeerrrsssiiitttyyy The official electronic file of this thesis or dissertation is maintained by the University Libraries on behalf of The Graduate School at Stony Brook University. ©©© AAAllllll RRRiiiggghhhtttsss RRReeessseeerrrvvveeeddd bbbyyy AAAuuuttthhhooorrr... Sensemo: An Adaptive Learning System Based on Real-Time User Emotions A Thesis presented by Karan Joshi to The Graduate School in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Engineering Stony Brook University December 2016 Stony Brook University The Graduate School Karan Joshi We, the thesis committe for the above candidate for the Master of Science degree, hereby recommend acceptance of this thesis Shan Lin - Thesis Advisor Assistant Professor, Department of Electrical and Computer Engineering Timothy J. Driscoll - Second Reader Adjunct Faculty, Department of Electrical and Computer Engineering This thesis is accepted by the Graduate School Charles Taber Dean of the Graduate School ii Abstract of the Thesis Sensemo: An Adaptive Learning System Based on Real-Time User Emotions by Karan Joshi Master of Science in Computer Engineering Stony Brook University 2016 Learning and education is an important part of everyone's life. E-learning systems and smart classrooms have recently become very popular because of the continuous effort to improve learning experience of students. This work studies a new emotion sensing based real-time adaptive learning system using Microsoft Band smartwatch based sensing system. We use three bio-sensors, namely photoplethysmogram (PPG) sensor, galvanic skin response (GSR) sensor and skin temperature sensor, which are available in Microsoft Band 2 to help the system read the bio-signals from a user in real time and unobtrusively.
    [Show full text]
  • Connect Compatible Apps and Devices to Go365®
    EARN REWARDS Connect compatible apps and devices to Go365® Go365.com GCHJHTFEN 0621 Table of contents Compatible fitness devices and apps 2 Earn Points by type of tracker 3 • Earn Points using your device 3 • Earn Points using your mobile app 4 • Earn Points for virtual coaching apps 5 How to connect to Go365 6 Important details after you’ve connected 7 How to earn Points 8 • Verified workouts and bonus Points 8 • Virtual well-being coaching activities 9 • Additional Go365 activities 10 1 Compatible fitness devices and apps Manufacturer Device Expresso All Expresso devices are compatible Fitbit All Fitbit wearable devices are compatible Garmin All Garmin wearable devices are compatible All Garmin Edge devices are compatible iHealth Edge Misfit All Misfit wearable devices are compatible Withings All Withings wearable, scale and blood (formerly Nokia Health) pressure devices are compatible Polar All Polar devices are compatible Qardio All Qardio scale and blood pressure devices are compatible Compatible apps Apple Health Health IQ Samsung Health Craving to Quit Life Fitness MyLife Daily Burn MyFitnessPal (formerly Stop, Breathe & Eat Right Now Noom Think) Fitbit RunDouble C25k Strava Fitbit Premium RunKeeper Unwinding Anxiety References to products and equipment in this material are not an endorsement or warranty by Humana or Go365 of the products or equipment. The manufacturer of the products or equipment is solely responsible for defects with or problems arising out of the use of the products or equipment. Such references to products and equipment are used as examples of products and equipment that are compatible with Go365, of which are subject to change at any time without notice.
    [Show full text]