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

Isabelle Poitras,Alexandre Campeau-Lecours,Catherine Mercier,Jade Clouâtre,Laurent J Bouyer,François Routhier

doi:10.3390/s20236767

Isabelle Poitras, Alexandre Campeau-Lecours + Show 4 more

Open Access

https://doi.org/10.3390/s20236767

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Abstract

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.

Highlights

The popularity of wearable devices to monitor physical activity (PA) has increased widely during the last decades
Sensors 2020, 20, 6767 there is a vast diversity of available miniature sensors and physical outcomes that can be extracted from such sensors
The objectives of the current study are to design and validate two activity quantification algorithms and an activity intensity classifier: (1) a fixed bandwidth algorithm, which replicates commercial activity intensity counts as closely as possible, allowing acceleration signal processing regardless of sampling rates; (2) a modifiable bandwidth algorithm that allows us to adapt the filter parameters, regardless of sampling rates; and (3) an activity intensity classification algorithm that allows the modification of cut-off values according to the studied population, thereby reducing potential saturation effects

Summary

Introduction

The popularity of wearable devices to monitor physical activity (PA) has increased widely during the last decades. From step counters to sleep monitors, are available to provide a broad spectrum of physiological measurements and feedbacks (e.g., step count [1], level of activity [2], upper limb activity count [3], distance traveled [4], heart rate history [5]). 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). This paper proposes an open-source alternative for rehabilitation that is compatible with several wearable devices and not dependent on manufacturer commercial decisions

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