A machine learning sequential model using wearable sensor data can accurately predict oxygen uptake kinetics across varying exercise intensities, allowing for nonintrusive cardiorespiratory monitoring.
Machine learning analysis of wearable sensor data with a sequential model, which utilized a receptive field of approximately 3 min to make instantaneous oxygen uptake estimations, accurately predicted oxygen uptake kinetics from moderate through to higher-intensity exercise. This innovation will enable nonintrusive cardiorespiratory monitoring over a wide range of exercise intensities encountered in vigorous training and competitive sports.
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Hedge et al. (2023) studied this question.
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