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Personalized Watch-Based Fall Detection Using a Collaborative Edge-Cloud Framework.

Anne Hee Ngu1, Vangelis Metsis1, Shuan Coyne1

  • 1Department of Computer Science, Texas State University, 601 University Drive, San Marcos 78666, TX, USA.

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Summary
This summary is machine-generated.

This study shows a smart system for fall detection on smartwatches, making it more practical for older adults. It enables real-time, personalized fall detection directly on the watch, improving safety and independence.

Keywords:
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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Gerontology

Background:

  • Current smart health apps often rely on smartphones paired with smartwatches, limiting practicality for older adults needing constant phone proximity.
  • Fall detection systems require immediate user interaction, which is challenging if the phone is misplaced during a fall.

Purpose of the Study:

  • To demonstrate the feasibility of a real-time, personalized deep learning-based fall detection system running entirely on a smartwatch.
  • To develop a practical and user-friendly fall detection solution for older adults, independent of a smartphone.

Main Methods:

  • Developed a collaborative edge-cloud framework for real-time fall detection on a smartwatch.
  • Automated the fall detection pipeline, designed a smartwatch UI, and implemented continuous data collection and personalization strategies.
  • Evaluated the system's usability with nine older adult participants.

Main Results:

  • Successfully demonstrated the feasibility of running a personalized deep learning fall detection system on a smartwatch.
  • The system architecture, automated pipeline, and UI were designed for smartwatch constraints.
  • Initial usability testing with older adults showed promising results.

Conclusions:

  • A smartwatch-based, personalized fall detection system is feasible and offers a more practical solution for older adults.
  • This approach enhances user independence and safety by removing the reliance on smartphones for fall detection.