Why the study?
Heart rate and heart rate variability from wearable monitors are physiologically relevant, but validating machine learning models to estimate maximal oxygen uptake from exercise and recovery segments in athletes was needed.
Can machine learning models using heart rate variability features accurately estimate maximal oxygen uptake in athletes?
Can machine learning models using heart rate variability features accurately estimate maximal oxygen uptake in athletes?
Heart rate variability features from exercise and recovery segments can be utilized by machine learning models to estimate maximal oxygen uptake in athletes, supporting the use of wearable heart rate monitors for cardiorespiratory fitness assessment.
May aid wearable VO2max estimation in athletes; leaves open prospective validation before clinical use.
Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes' well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model's accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors.
No takes yet. Share an insight, caveat, or question.
Balakarthikeyan et al. (2023) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: