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ORIGINAL RESEARCH article

Front. Sports Act. Living
Sec. Sports Science, Technology and Engineering
Volume 6 - 2024 | doi: 10.3389/fspor.2024.1448243
This article is part of the Research Topic Training in Sports: The Role of Artificial Intelligence and Machine Learning View all articles

Feature Importance for Estimating Rating of Perceived Exertion from Cardiorespiratory Signals Using Machine Learning

Provisionally accepted
Runbei Cheng Runbei Cheng 1*phoebe haste phoebe haste 1Elyse Levens Elyse Levens 1Jeroen Bergmann Jeroen Bergmann 1,2
  • 1 University of Oxford, Oxford, United Kingdom
  • 2 University of Southern Denmark, Odense, Denmark

The final, formatted version of the article will be published soon.

    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. 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 fully-connected 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). 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. Future works that aim to estimate RPE using wearable sensors should therefore consider using a combination of cardiovascular and respiratory data.

    Keywords: Fatigue, Heart Rate, breathing, Ventilation, machine learning, physical activity, Yo-Yo test

    Received: 13 Jun 2024; Accepted: 10 Sep 2024.

    Copyright: © 2024 Cheng, haste, Levens and Bergmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Runbei Cheng, University of Oxford, Oxford, United Kingdom

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