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Related Experiment Video

Updated: Jun 11, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Published on: February 25, 2013

Identifying Walking Trips Using GPS Data.

Gi-Hyoug Cho1, Daniel A Rodríguez, Kelly R Evenson

  • 1Department of City and Regional Planning, University of North Carolina, Chapel Hill, NC 27599, USA. gicho@email.unc.edu

Medicine and Science in Sports and Exercise
|June 29, 2010
PubMed
Summary
This summary is machine-generated.

This study developed an algorithm to identify outdoor walking trips using portable global positioning system (GPS) devices. The algorithm accurately detects walking trips over 5 minutes, improving physical activity monitoring.

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Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

Area of Science:

  • Physical activity monitoring
  • Biomedical engineering
  • Public health research

Background:

  • Accurate measurement of physical activity, particularly walking, is crucial for health research.
  • Portable global positioning system (GPS) units offer a promising method for objective assessment of outdoor activities.
  • Developing robust algorithms is essential to reliably distinguish walking trips from other movements using GPS data.

Purpose of the Study:

  • To develop and validate algorithms for identifying outdoor walking trips using portable global positioning system (GPS) units in free-living conditions.
  • To establish the accuracy and reliability of GPS-based walking trip detection compared to self-reported data.
  • To optimize parameters for distinguishing walking trips based on GPS data characteristics.

Main Methods:

  • A calibration phase involved determining the optimal algorithm from 35 person-days of GPS and diary data.
  • Key algorithm parameters included trip duration, gap between points, speed, displacement, and merging criteria.
  • A validation phase applied the best algorithm to an independent dataset of 136 person-days.

Main Results:

  • The preferred algorithm identified 90% of GPS-detected trips in the diary during calibration and 86% during validation.
  • The algorithm successfully identified 81% of diary-reported trips from GPS data in calibration and 77% in validation.
  • Optimal parameters included a minimum 5-minute trip duration, speed between 2-8 km/h, and specific displacement and gap criteria.

Conclusions:

  • The developed algorithm reliably identifies free-living outdoor walking trips exceeding 5 minutes in duration.
  • Improving GPS recording intervals and participant compliance can enhance walking trip detection accuracy.
  • Future research should explore integrating accelerometers with GPS to further improve walking detection.