IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0118723.html
   My bibliography  Save this article

Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118723
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0118723&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0118723?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0118723. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.