A Transparent Method for Step Detection Using an Acceleration Threshold

in Journal for the Measurement of Physical Behaviour
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  • 1 Department of Kinesiology, California State University, Long Beach, Long Beach, CA, USA
  • | 2 Department of Counseling, Health and Kinesiology, Texas A&M University-San Antonio, San Antonio, TX, USA
  • | 3 Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
  • | 4 Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA
  • | 5 Department of Kinesiology, The University of Alabama, Tuscaloosa, AL, USA
  • | 6 Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
  • | 7 School of Biological and Population Health Sciences, Oregon State University, Corvallis, OR, USA
  • | 8 Falk College, Syracuse University, Syracuse, NY, USA
  • | 9 Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, USA
  • | 10 College of Health and Human Services, The University of North Carolina at Charlotte, Charlotte, NC, USA
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Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply does not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and nonambulatory activities. Seventy-five adults (21–39 years) completed seven simulated activities of daily living (e.g., sitting, vacuuming, folding laundry) and an incremental treadmill protocol from 0.22 to 2.2 m/s. Directly observed steps were hand-tallied. Participants wore GENEActiv and ActiGraph accelerometers, one of each on their waist and on their nondominant wrist. Raw acceleration (g) signals from the anterior–posterior, medial–lateral, vertical, and vector magnitude directions were assessed separately for each device. Signals were demeaned across all activities and band-pass filtered (0.25, 2.5 Hz). Steps were detected via peak picking, with optimal thresholds (i.e., minimized absolute error from accumulated hand counted) determined by iterating minimum acceleration values to detect steps. Step counts were converted into cadence (steps/minute), and k-fold cross-validation quantified error (root mean squared error [RMSE]). We report optimal thresholds for use of either device on the waist (threshold = 0.0267g) and wrist (threshold = 0.0359g) using the vector magnitude signal. These thresholds yielded low error for the waist (RMSE < 173 steps, ≤2.28 steps/min) and wrist (RMSE < 481 steps, ≤6.47 steps/min) across all activities, and outperformed ActiLife’s proprietary algorithm (RMSE = 1,312 and 2,913 steps, 17.29 and 38.06 steps/min for the waist and wrist, respectively). The thresholds reported herein provide a simple, transparent framework for step detection using accelerometers during treadmill ambulation and activities of daily living for waist- and wrist-worn locations.

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