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Inferring Destinations and Activity Types of Older Adults From GPS Data: Algorithm Development and Validation.

Sayeh Bayat1,2, Gary Naglie2,3,4,5,6, Mark J Rapoport7,8

  • 1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.

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Summary
This summary is machine-generated.

This study shows Global Positioning System (GPS) technology can accurately identify older adults' outdoor destinations and activities. This advancement offers potential for better health and aging research by understanding mobility patterns.

Keywords:
GPSactivity typeslife spacemachine learningolder adultsoutdoor mobility

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Area of Science:

  • Gerontology
  • Geographic Information Systems (GIS)
  • Health Informatics

Background:

  • Outdoor mobility is crucial for assessing older adults' functional status.
  • Existing Global Positioning System (GPS) applications focus on spatiotemporal mobility aspects.
  • A gap exists in classifying older adults' outdoor mobility based on semantic dimensions (intentions, motivations).

Purpose of the Study:

  • To assess the feasibility of using GPS to determine semantic dimensions of older adults' outdoor mobility.
  • To identify destinations and infer activity types (e.g., food, shopping, sport) from GPS data.

Main Methods:

  • Five healthy participants (≥65 years) used GPS devices for 4 weeks.
  • Participants maintained travel diaries to record excursion details.
  • An algorithm was developed to extract destinations and infer activity types from GPS data, validated against diaries.

Main Results:

  • GPS successfully detected stop locations with an 87% F1 score.
  • Home locations were identified with an average accuracy of 40.18 meters.
  • Activity inference achieved an 86% F1 score, with accuracy varying by neighborhood Walk Score.

Conclusions:

  • GPS technology demonstrates accuracy in determining semantic dimensions of outdoor mobility.
  • Further refinement is necessary for robust clinical adoption of GPS-based mobility assessment systems.