This paper is in the following e-collection/theme issue:

Big Data Analytics for Elder Care (38) Innovations and Technology for Physical Activity Education (936) Machine Learning (2095) mHealth for Data Collection and Research (1207) mHealth for Wellness, Behavior Change and Prevention (3339) Physical Activity for Older People (288)

Published on in Vol 3, No 2 (2020): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18008, first published .
Inferring Destinations and Activity Types of Older Adults From GPS Data: Algorithm Development and Validation

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

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

Authors of this article:

Sayeh Bayat1, 2 Author Orcid Image ;   Gary Naglie2, 3, 4, 5, 6 Author Orcid Image ;   Mark J Rapoport7, 8 Author Orcid Image ;   Elaine Stasiulis5, 9 Author Orcid Image ;   Belkacem Chikhaoui10 Author Orcid Image ;   Alex Mihailidis1, 2, 11 Author Orcid Image
Article Authors Cited by (11) Tweetations (7) Metrics

Journals

  1. Bayat S, Mihailidis A. Outdoor life in dementia: How predictable are people with dementia in their mobility?. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 2021;13(1) View
  2. Cullen A, Mazhar M, Smith M, Lithander F, Ó Breasail M, Henderson E. Wearable and Portable GPS Solutions for Monitoring Mobility in Dementia: A Systematic Review. Sensors 2022;22(9):3336 View
  3. Bayat S, Naglie G, Rapoport M, Stasiulis E, Widener M, Mihailidis A. A GPS-Based Framework for Understanding Outdoor Mobility Patterns of Older Adults with Dementia: An Exploratory Study. Gerontology 2022;68(1):106 View
  4. Verma R, Mittal S, Lei Z, Chen X, Ukkusuri S. Comparison of home detection algorithms using smartphone GPS data. EPJ Data Science 2024;13(1) View
  5. Saniger A, López D, Delón C, Ruiz O. Mobile data for studying public space, and trips in Mexico City: a study case of six suburban zones. Data & Policy 2024;6 View
  6. Hashida K, Drattell J, Lynall R, Devos H, Gore R, Schmidt J. Examination of naturalistic driving behavior and risk events across concussion recovery. Transportation Research Part F: Traffic Psychology and Behaviour 2025;111:211 View
  7. Krataithong P, Anutariya C, Buranarach M. A Tourist Behavior Analysis Using Probabilistic-Based Tourist Trip Inference from Taxi GPS and Social Media Data. New Generation Computing 2025;43(4) View
  8. DeJohn A, Bowie A. Negotiating mobility: Methodological insights from older adults' activity diaries. Social Science & Medicine 2026;388:118726 View
  9. Hafezifar R, Alizadeh S, Dickerson A, Vrkljan B, Babulal G, Bayat S. The neighbourhood built environment affects driving behaviours of older adults: a combined geographic information systems and machine learning method. Cities & Health 2025:1 View
  10. Ng Y, Zhao F, Ho L, Tay L, Shum E, Latib A, Choo S, Chew S, Leong T, Pua Y, Yuen B, Joyce S, Yang X, Chan A, Maulod A, Wong Y, Soh D, Balan R, Low L, Thumboo J, Ding Y, Hoenig H, Lin S. Elderly Activity Life-Space Envelopes (EASE): Development and Feasibility of a Comprehensive, Integrated Protocol for Life-Space Mobility Research in Population Health. JMIR Research Protocols 2025;14:e79308 View

Conference Proceedings

  1. Fan X, Li G, Lin Z, Hu Y, Liu Y, Jiang T, Yin Z, Qian F, Wang S, Chan S. Proceedings of the 30th Annual International Conference on Mobile Computing and Networking. Experiences of Deploying a Citywide Crowdsourcing Platform to Search for Missing People with Dementia View