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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 23; p. 6767
• Main Authors: Poitras, Isabelle, Clouâtre, Jade, Bouyer, Laurent J., Routhier, François, Mercier, Catherine, Campeau-Lecours, Alexandre
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.11.2020; MDPI
• Subjects: Acceleration; Accelerometers; activity level classification; activity level quantification; Algorithms; Bandwidths; Cerebral palsy; Classification; Data processing; Exercise; Feedback; Heart rate; Humans; Parkinson's disease; Rehabilitation; rehabilitation engineering; rehabilitation technologies; Sensors; Signal processing; Tremor (Muscular contraction); Wearable computers; Wearable Electronic Devices; wearable sensors; activity level classification; activity level quantification; rehabilitation technologies; rehabilitation engineering; accelerometers; wearable sensors; physical activity quantification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20236767

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.