Cross-Domain Classification of Physical Activity Intensity: An EDA-Based Approach Validated by Wrist-Measured Acceleration and Physiological Data

Authors:
Open Access
Publisher website
Google Scholar
Abstract
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.
Funding Information
  • Ministero dell’Istruzione, dell’Università e della Ricerca (CUP: I36G170003800)
  • Joint Programming Initiative More Years, Better Lives (PAAL_JTC2017)
  • Ministero delle imprese e del made in Italy (project nr. F/180016/01-05/X43)

This publication has 43 references indexed in Scilit: