Step detection and activity recognition accuracy of seven physical activity monitors

STORM, F, HELLER, Ben and MAZZA, C (2015). Step detection and activity recognition accuracy of seven physical activity monitors. PLOS ONE, 10 (3), e0118723.

[img]
Preview
PDF
Heller_Step_detection_and_activity_recognition_accuracy.pdf - Published Version
Creative Commons Attribution Non-commercial No Derivatives.

Download (482kB) | Preview
Official URL: http://dx.doi.org/10.1371/journal.pone.0118723
Link to published version:: https://doi.org/10.1371/journal.pone.0118723

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.

Item Type: Article
Research Institute, Centre or Group - Does NOT include content added after October 2018: Centre for Sports Engineering Research
Identification Number: https://doi.org/10.1371/journal.pone.0118723
Page Range: e0118723
Depositing User: Carole Harris
Date Deposited: 30 Mar 2015 11:23
Last Modified: 18 Mar 2021 07:51
URI: https://shura.shu.ac.uk/id/eprint/9569

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics