Feature importance for estimating rating of perceived exertion from cardiorespiratory signals using machine learning.

Autor:	Cheng R; Natural Interaction Lab, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom., Haste P; Natural Interaction Lab, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom., Levens E; Natural Interaction Lab, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom., Bergmann J; Natural Interaction Lab, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.; Department of Technology and Innovation, University of Southern Denmark, Odense, Denmark.
Jazyk:	angličtina
Zdroj:	Frontiers in sports and active living [Front Sports Act Living] 2024 Sep 24; Vol. 6, pp. 1448243. Date of Electronic Publication: 2024 Sep 24 (Print Publication: 2024).
DOI:	10.3389/fspor.2024.1448243
Abstrakt:	Introduction: The purpose of this study is to investigate the importance of respiratory features, relative to heart rate (HR), when estimating rating of perceived exertion (RPE) using machine learning models. Methods: A total of 20 participants aged 18 to 43 were recruited to carry out Yo-Yo level-1 intermittent recovery tests, while wearing a COSMED K5 portable metabolic machine. RPE information was collected throughout the Yo-Yo test for each participant. Three regression models (linear, random forest, and a multi-layer perceptron) were tested with 8 training features (HR, minute ventilation (VE), respiratory frequency (Rf), volume of oxygen consumed (VO2), age, gender, weight, and height). Results: Using a leave-one-subject-out cross validation, the random forest model was found to be the most accurate, with a root mean square error of 1.849, and a mean absolute error of 1.461 ± 1.133. Feature importance was estimated via permutation feature importance, and VE was found to be the most important for all three models followed by HR. Discussion: Future works that aim to estimate RPE using wearable sensors should therefore consider using a combination of cardiovascular and respiratory data. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (© 2024 Cheng, Haste, Levens and Bergmann.)
Databáze:	MEDLINE
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