Understanding the Lifestyle of Older Population: Mobile Crowdsensing Approach
By Sumudu Hasala Marakkalage, Serhad Sarica, Billy Pik Lik Lau, Sanjana Kadaba Viswanath, Thirunavukarasu Balasubramaniam, Chau Yuen, Belinda Yuen, Jianxi Luo, Richi Nayak. 2019
34 citations recorded by the source corpus.
In this paper, we present a mobile crowdsensing approach to understand the daily lifestyle of the older population in Singapore. By implementing novel clustering, sensor fusion, and user profiling techniques to analyze the multisensor data (location, noise, and light) collected from a smartphone application, we identified the travel patterns at several points of interest (POI), the impact of travel frequency for certain POI, and three main user profiles. The results show that older adults mostly spend time at food courts and community centers in their home neighborhood, but they travel away from the neighborhood for healthcare and religious purposes. We found that POIs have more visits if they are easily accessible (in terms of travel time from home) regardless of the distance from home.
Subfields: Automotive Engineering, Transportation
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Citations in the corpus
- A survey of data fusion in smart city applications (2019, background): “• S3: Participatory Data Sources Participatory data sources include crowdsensing [90], [91] and crowdsourcing [92], [93] data contributed by the personal devices, e.g. mobile phones, wearable devices, tablets, etc. of the users in smart city.”