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Article

Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors

1
Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, QC G1M 2S8, Canada
2
Department of Rehabilitation, Laval University, Quebec City, QC G1V 0A6, Canada
3
Department of Mechanical Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(23), 6767; https://doi.org/10.3390/s20236767
Received: 30 October 2020 / Revised: 17 November 2020 / Accepted: 23 November 2020 / Published: 26 November 2020
(This article belongs to the Collection Inertial Sensors and Applications)
Background: A popular outcome in rehabilitation studies is the activity intensity count, which is typically measured from commercially available accelerometers. However, the algorithms are not openly available, which impairs long-term follow-ups and restricts the potential to adapt the algorithms for pathological populations. The objectives of this research are to design and validate open-source algorithms for activity intensity quantification and classification. Methods: Two versions of a quantification algorithm are proposed (fixed [FB] and modifiable bandwidth [MB]) along with two versions of a classification algorithm (discrete [DM] vs. continuous methods [CM]). The results of these algorithms were compared to those of a commercial activity intensity count solution (ActiLife) with datasets from four activities (n = 24 participants). Results: The FB and MB algorithms gave similar results as ActiLife (r > 0.96). The DM algorithm is similar to a ActiLife (r ≥ 0.99). The CM algorithm differs (r ≥ 0.89) but is more precise. Conclusion: The combination of the FB algorithm with the DM results is a solution close to that of ActiLife. However, the MB version remains valid while being more adaptable, and the CM is more precise. This paper proposes an open-source alternative for rehabilitation that is compatible with several wearable devices and not dependent on manufacturer commercial decisions. View Full-Text
Keywords: wearable sensors; activity level quantification; activity level classification; rehabilitation technologies; rehabilitation engineering; accelerometers; physical activity quantification wearable sensors; activity level quantification; activity level classification; rehabilitation technologies; rehabilitation engineering; accelerometers; physical activity quantification
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MDPI and ACS Style

Poitras, I.; Clouâtre, J.; Bouyer, L.J.; Routhier, F.; Mercier, C.; Campeau-Lecours, A. Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors. Sensors 2020, 20, 6767. https://doi.org/10.3390/s20236767

AMA Style

Poitras I, Clouâtre J, Bouyer LJ, Routhier F, Mercier C, Campeau-Lecours A. Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors. Sensors. 2020; 20(23):6767. https://doi.org/10.3390/s20236767

Chicago/Turabian Style

Poitras, Isabelle; Clouâtre, Jade; Bouyer, Laurent J.; Routhier, François; Mercier, Catherine; Campeau-Lecours, Alexandre. 2020. "Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors" Sensors 20, no. 23: 6767. https://doi.org/10.3390/s20236767

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