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

Jirapond Muangprathub1,2, Anirut Sriwichian1, Apirat Wanichsombat1

  • 1Faculty of Science and Industrial Technology, Surat Thani Campus, Prince of Songkla University, Surat Thani 84000, Thailand.

International Journal of Environmental Research and Public Health
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Summary

This study developed an integrated elderly tracking system using machine learning for real-time activity monitoring and geolocation. The system achieved 96.40% accuracy in classifying elderly activities, enhancing safety and care.

Keywords:
elderly tracking systemhuman activity recognition systemk-nearest neighbormachine learningwearable sensors

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Area of Science:

  • Gerontology and Health Informatics
  • Machine Learning Applications in Healthcare
  • Assistive Technology for Elderly Care

Background:

  • Growing elderly population necessitates advanced health and activity monitoring systems.
  • Existing systems are insufficient to meet the increasing demand for comprehensive elderly care.
  • Need for integrated solutions covering activity tracking, geolocation, and personal information.

Purpose of the Study:

  • To develop an innovative elderly tracking system integrating multiple technologies and machine learning.
  • To enhance real-time monitoring capabilities for both indoor and outdoor environments.
  • To provide a comprehensive solution for elderly care, including data collection, tracking, and emergency alerts.

Main Methods:

  • Integration of multiple technologies for activity tracking and geolocation.
  • Application of machine learning, specifically the k-nearest neighbor (k-NN) model, for activity classification.
  • Collaboration with local agencies for system planning and development, including case study testing.

Main Results:

  • The k-nearest neighbor (k-NN) model with k=5 demonstrated high effectiveness, achieving 96.40% accuracy in classifying nine distinct elderly activities.
  • The developed system enables real-time monitoring, provides timely alerts, and displays elderly information spatially.
  • The system facilitates emergency help requests via a messaging device.

Conclusions:

  • The developed elderly tracking system effectively supports elderly care through data collection, real-time monitoring, and notifications.
  • The system provides valuable supporting information to relevant agencies involved in elderly care.
  • This integrated approach enhances the safety, independence, and quality of life for the elderly population.