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Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors

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  • Fabio A Storm
  • Ben W Heller
  • Claudia Mazzà

Abstract

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration.The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Suggested Citation

  • Fabio A Storm & Ben W Heller & Claudia Mazzà, 2015. "Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-13, March.
  • Handle: RePEc:plo:pone00:0118723
    DOI: 10.1371/journal.pone.0118723
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    Cited by:

    1. Xiheng Zhang & Yongkang Wong & Mohan S Kankanhalli & Weidong Geng, 2019. "Hierarchical multi-view aggregation network for sensor-based human activity recognition," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-20, September.
    2. Jochen Kressler & Joshua Koeplin-Day & Benedikt Muendle & Brice Rosby & Elizabeth Santo & Antoinette Domingo, 2018. "Accuracy and precision of consumer-level activity monitors for stroke detection during wheelchair propulsion and arm ergometry," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-15, February.

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