Key result
Support Vector Machine applied to physiological and acceleration data effectively predicted physical activity intensity, while Bagged Tree performed best using only Electrodermal Activity data.
Why the study?
Exerted physical effort is subjectively perceived among different individuals, warranting an out-of-laboratory approach using a wrist-worn device to quantitatively classify perceived physical activity intensity.
Can machine learning algorithms using data from a wrist-worn device accurately classify the perceived intensity of physical effort?
Can machine learning algorithms using data from a wrist-worn device accurately classify the perceived intensity of physical effort?
Machine learning algorithms can effectively classify perceived physical exertion using physiological and electrodermal activity data from wrist-worn devices.
Supports ML classification of exertion from wrist devices in research; leaves open clinical validation and adoption.
Performing regular physical activity positively affects individuals’ quality of life in both the short- and long-term and also contributes to the prevention of chronic diseases. However, exerted effort is subjectively perceived from different individuals. Therefore, this work explores an out-of-laboratory approach using a wrist-worn device to classify the perceived intensity of physical effort based on quantitative measured data. First, the exerted intensity is classified by two machine learning algorithms, namely the Support Vector Machine and the Bagged Tree, fed with features computed on heart-related parameters, skin temperature, and wrist acceleration. Then, the outcomes of the classification are exploited to validate the use of the Electrodermal Activity signal alone to rate the perceived effort. The results show that the Support Vector Machine algorithm applied on physiological and acceleration data effectively predicted the relative physical activity intensities, while the Bagged Tree performed best when the Electrodermal Activity data were the only data used.
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Poli et al. (2021) studied Physical activity intensity. Machine learning algorithms (Support Vector Machine and Bagged Tree) vs. Electrodermal Activity signal alone was evaluated on Classification of perceived intensity of physical effort. Support Vector Machine applied to physiological and acceleration data effectively predicted physical activity intensity, while Bagged Tree performed best using only Electrodermal Activity data.
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