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

Abstract

IntroductionThe 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.MethodsA 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).ResultsUsing 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.DiscussionFuture works that aim to estimate RPE using wearable sensors should therefore consider using a combination of cardiovascular and respiratory data.