A Novel Elderly Tracking System Using Machine Learning to Classify Signals from Mobile and Wearable Sensors

Jirapond Muangprathub, Anirut Sriwichian, Apirat Wanichsombat, Siriwan Kajornkasirat, Pichetwut Nillaor and Veera Boonjing
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Jirapond Muangprathub: Faculty of Science and Industrial Technology, Surat Thani Campus, Prince of Songkla University, Surat Thani 84000, Thailand
Anirut Sriwichian: Faculty of Science and Industrial Technology, Surat Thani Campus, Prince of Songkla University, Surat Thani 84000, Thailand
Apirat Wanichsombat: Faculty of Science and Industrial Technology, Surat Thani Campus, Prince of Songkla University, Surat Thani 84000, Thailand
Siriwan Kajornkasirat: Faculty of Science and Industrial Technology, Surat Thani Campus, Prince of Songkla University, Surat Thani 84000, Thailand
Pichetwut Nillaor: Faculty of Commerce and Management, Trang Campus, Prince of Songkla University, Trang 92000, Thailand
Veera Boonjing: Department of Computer Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand

IJERPH, 2021, vol. 18, issue 23, 1-19

Abstract: A health or activity monitoring system is the most promising approach to assisting the elderly in their daily lives. The increase in the elderly population has increased the demand for health services so that the existing monitoring system is no longer able to meet the needs of sufficient care for the elderly. This paper proposes the development of an elderly tracking system using the integration of multiple technologies combined with machine learning to obtain a new elderly tracking system that covers aspects of activity tracking, geolocation, and personal information in an indoor and an outdoor environment. It also includes information and results from the collaboration of local agencies during the planning and development of the system. The results from testing devices and systems in a case study show that the k-nearest neighbor (k-NN) model with k = 5 was the most effective in classifying the nine activities of the elderly, with 96.40% accuracy. The developed system can monitor the elderly in real-time and can provide alerts. Furthermore, the system can display information of the elderly in a spatial format, and the elderly can use a messaging device to request help in an emergency. Our system supports elderly care with data collection, tracking and monitoring, and notification, as well as by providing supporting information to agencies relevant in elderly care.

Keywords: machine learning; elderly tracking system; human activity recognition system; k-nearest neighbor; wearable sensors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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