Step Counts From Satellites: Methods for Integrating Accelerometer and GPS Data for More Accurate Measures of Pedestrian Travel

in Journal for the Measurement of Physical Behaviour
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  • 1 University of California, Los Angeles
  • | 2 Max Planck Institute for Evolutionary Anthropology
  • | 3 Duke University
  • | 4 University of Dar es Salaam
  • | 5 University of Houston
  • | 6 University of Southern California
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The rapid adoption of lightweight activity tracking sensors demonstrates that precise measures of physical activity hold great value for a wide variety of applications. The corresponding growth of physical activity data creates an urgent need for methods to integrate such data. In this paper, we demonstrate methods for 1) synchronizing accelerometer and Global Positioning System (GPS) data with optimal corrections for device-related time drift, and 2) producing principled estimates of step counts from GPS data. These methods improve the accuracy of time-resolved physical activity measures and permit pedestrian travel from either sensor to be expressed in terms of a common currency, step counts. We show that sensor-based estimates of step length correspond well with expectations based on independent measures, and functional relationships between step length, height, and movement speed expected from biomechanical models. Using 123 person-days of data in which Hadza hunter-gatherers wore both GPS devices and accelerometers, we find that GPS-based estimates of daily step counts have a good correspondence with accelerometer-recorded values. A multivariate linear model predicting daily step counts from distance walked, mean movement speed, and height has an R 2 value of 0.96 and a mean absolute percent error of 16.8% (mean absolute error = 1,354 steps; mean steps per day = 15,800; n = 123). To best represent step count estimation error, we fit a Bayesian model and plot the distributions of step count estimates it generates. Our methods more accurately situate accelerometer-based measures of physical activity in space and time, and provide new avenues for comparative research in biomechanics and human movement ecology.

Wood and Harris are with the Department of Anthropology, University of California, Los Angeles. Wood is also with, and Beheim is with, the Department of Human Behavior, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany. Pontzer is with the Department of Evolutionary Anthropology, Global Health Institute, Duke University, Durham, NC. Mabulla is with the Department of Archaeology, University of Dar es Salaam, Dar es Salaam, Tanzania. Hamilton and Zderic are with the Texas Obesity Research Center and Department of Health and Human Performance, University of Houston, Houston, TX. Hamilton is also with the Department of Biology and Biochemistry, University of Houston, Houston, TX. Raichlen is with the Human and Evolutionary Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA.

Wood ( is corresponding author.

Supplementary Materials

    • Supplementary Materials (PDF 182 KB)