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Identifying Points of Interest for Elderly in Singapore Through Mobile Crowdsensing

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Identifying Points of Interest for Elderly in Singapore Through Mobile Crowdsensing Identifying Points of Interest for Elderly in Singapore through Mobile Crowdsensing Marakkalage Sumudu Hasala1, Billy Lau Pik Lik1, Viswanath Sanjana Kadaba1, Balasubramaniam Thirunavukarasu3, Chau Yuen1, Belinda Yuen2, and Richi Nayak3 1Engineering Product Development, Singapore University of Technology and Design, Singapore 2Lee Kuan Yew Centre for Innovative Cities, Singapore University of Technology and Design, Singapore 3School of Electrical Engineering and Computer Science, Queensland University of Technology, Australia [email protected], fbilly lau,[email protected] Keywords: Big Data; Crowdsensing; POI; Voronoi Diagrams; Heat Maps Abstract: This paper introduces a crowdsensing approach to identify the points of interest (POI) among the elderly population in Singapore. We have developed a smartphone application, which passively collects sensors’ information (e.g. GPS location) on users’ mobile devices. Using such information, we can identify popular regions and places among the elderly that could be useful for city planner in preparation for aging population. Our results demonstrate different check-in patterns of various POI, and the elderly spend nearly 70% of non- home duration around their neighborhood. 1 INTRODUCTION this paper, adopts a smartphone based mobile applica- tion as the tool to better understand the daily activity of an elderly. Using the smartphone sensor informa- Elderly population is a key component in any so- tion collected through mobile application, we prove ciety. In Singapore, the number of citizens aged 65 that it is possible to identify the Regions of Interest and above is increasing rapidly. In fact, the resident (ROI) and Points of Interest (POI) of elderly in Sin- Old-Age Support Ratio (persons aged 20 − 64 years gapore. per elderly aged 65 years and over) has significantly The remaining sections of this paper are structured reduced from 9 to 5:7 over the years of 2000 to 2015 as follows. Section 2, discusses the literature review respectively (Department of Statistics, 2016). Rising and related work done previously. Next, Section 3 elderly population is a challenge to address since their discusses the methodology which is used to collect requirements and interests should be understood in or- and analyse data from elderly. Section 4 describes the der to provide them a better quality of life. According insights about ROI from the analysed data. Section 5 to (Capella and Greco, 1987), elderly have tendency presents POI extracted from each ROI. Finally, Sec- of travelling around in their leisure time, which leads tion 6, concludes the paper with a summary of find- to the thinking that it is important to identify their ings, and possible future work. preference of places to visit. In order to achieve such information we need to reach the elderly and com- municate to gather required information from them. Rather than conducting verbal or written surveys, the 2 LITERATURE REVIEW more convenient approach is to utilize the ubiquitous nature of smartphones among the elderly as a resource There has been previous research done to to collect data from heterogeneous sensors available track user locations using mobile applications. in smartphones. According to study in (Pang et al., Tourist tracking through smartphone location data 2014), elderly in Singapore have positive attitude to- (Viswanath et al., 2014) has been done in order to wards smartphones and have found them to be use- identify travel sites of tourists and to rate tourist sites ful and entertaining. Hence, a smartphone applica- based on user surveys conducted through the mobile tion based approach has the potential in quantitatively application. In the context of smart homes, previous identifying elderly lifestyle. The work presented in work has been carried out for proactive environment change for elderly (Helal et al., 2003), cloud based our intention is to utilize all the collected information real-time activity tracking in home (Fahim et al., in order to extract deep insights about user behaviour. 2012) which helps to remotely monitor elderly who stay at home, and RFID based location tracking (Kim et al., 2013). An adaptable interface for smartphones 3.2 Elderly Data Collection which is customized to the elderly has been developed in (Arab et al., 2013). Helping people with limited The recruitment of target participant group for the mobility (e.g. elderly) by suggesting accessible ur- study (elderly who use Android smartphones and re- ban paths using crowdsourced data has been done in side in Bukit Panjang area of Singapore) was done (Mirri et al., 2014). A passive information gathering in public locations around Bukit Panjang. All the system using mobile terminals such as smartphone data were collected for one month of minimum pe- applications, emails, and phonecalls, has been done riod with the written consent from every participant in to gather knowledge and experience from elderly and the study. Participants’ personal identity is used only transfer to younger generation (Hiyama et al., 2013). during registration. The data collection and analysis In (Ghiani et al., 2013), a platform has been devel- is done through a unique auto-generated device ID to oped to support elderly to stay active in life in order ensure privacy of the participants. Figure 1 shows the to improve their healthy living. flow of elderly data collection. To the best of our knowledge, there is no previous work done to identify the ROI and POI of elderly, us- ing smartphone based mobile applications. Our focus is not only for the elderly who stay at home, but also for the elderly who prefer to travel around their neigh- bourhood or further. Therefore, this paper presents an approach to understand lifestyle of elderly, using the data collected through smartphone applications, and analysing them. 3 METHODOLOGY Figure 1: Elderly Data Collection A user-friendly smartphone application that runs on Android platform, is designed and developed (SUTDDev, 2015) to collect data from elderly. More 3.3 Big Data Analysis than 100 people, the majority aged 50 and above, who reside in Bukit Panjang area of Singapore par- ticipated in the study by installing the application on Aquired data from the mobile phones of the elderly their mobile phones. In this paper, we reported only need to be analysed in order to gather meaningful in- the data from 50 participants, whose collected data is sights. Data analysis was done using back-end Java consistent throughout the study period. There are 3 application and MATLAB software package. Af- major stages in the approach of identifying ROI and ter the data is analysed, tools such as Google Maps, POI, namely, Mobile Application Development, El- Voronoi Diagrams, and Heat Maps are used to visual- derly Data Collection, and Big Data Analysis. ize the gathered insights, so that it is possible to iden- tify the ROI and POI. 3.1 Mobile Application Development Data pre-processing is done in 2 major stages such as (1) Denoise which removes duplicate data records The smartphone application, which is used to collect and anomalies occured due to location accuracies, and data from participants is an improved version of the (2) Time sync which aligns data from different tables application introduced in (Viswanath et al., 2014). in relevant time slots. The application collects data such as GPS location After pre-processing stage, the raw location data (including latitude, longitude, and accuracy), activity were clustered using k-means clustering technique data from Google API, noise level of the environment, (Billy Lau, tted) and generated a summary database device battery level, and light sensor values. In the for each user, which contains the parameters as shown process of identifying ROI and POI, in this paper, we in Table 1. A total 1781 number of clusters were gen- utilized only the location related data. In future work, erated from raw data of all 50 users. Percentage Home Stay Duration and Ages of Users Table 1: Parameters in the summary database 80 120 Female Male Age 75 100 Parameter Remarks 70 80 ID Unique user ID 65 60 Gender Male/Female 60 Percentage Age (Years) Age User’s age 40 55 Location Latitude and Longitude of cluster location Purpose Purpose of visit (e.g. H-Home, G-Grocery, W-Work) 20 50 Duration Time spent in particular cluster 0 45 0 5 10 15 20 25 30 35 40 45 50 Visits Number of visits to particular cluster Users Total Time Duration of first and last record from user Figure 2: Percentage of home stay duration, gender, and age of each user. 4 REGIONS OF INTEREST ROI analysis is done in three categories. First, we identify the percentage of home stay duration of each user out of their total participation time. Next we identify the ROI across user neighbourhood, which is Bukit Panjang, Singapore. Further, an analysis is done across Singapore to identify islandwide ROI. Figure 3: Percentage of number of visits and duration across ROI are analysed using a technique called Voronoi regions in Singapore. Diagrams. In a Voronoi diagram, a plane is parti- tioned into n points such that each polygon contains one seed and every point in each polygon is closer to 4.2 Across Neighbourhood the seed inside each polygon than to any other one. These polygons are called Voronoi zones. We divided Bukit Panjang area into 12 Voronoi Zones, in order to get an understanding about partici- pants’ home region since all of them are residents of 4.1 Home Stay Duration Bukit Panjang. The Voronoi zone distibution, zone number, and number of participant homes in each Voronoi zone is shown in Figure 3. Figure 2 shows the percentage of time each partici- pant spent at home, out of his/her total participation duration for data collection. It also shows the age and gender of each of the participant. The oldest partici- pant is 75 years old, and the average age for partici- pants is 60 years.
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