Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities

TL;DR

This study develops a temporal convolutional network to accurately predict oxygen uptake from wearable sensors during exercise, enabling real-time, non-laboratory monitoring of cardiorespiratory function across various exercise intensities.

Contribution

The paper introduces a novel TCN-based model that predicts VO$_2$ from wearable sensors with high accuracy, extending monitoring capabilities outside laboratory settings.

Findings

TCN model predicts VO$_2$ with less than 3% deviation across exercise intensities.

Second-by-second activity classification achieves 94.1% accuracy.

Optimal history length for prediction is approximately 218 seconds.

Abstract

Oxygen consumption (VO$_2$) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, VO$_2$ monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here, we investigate temporal prediction of VO$_2$ from wearable sensors during cycle ergometer exercise using a temporal convolutional network (TCN). Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth VO$_2$ from a metabolic system on twenty-two young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of VO$_2$ dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of VO$_2$. Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO$_2$ A), with 187 s, 97 s, and 76 s yielding less than 3% deviation from the optimal validation loss. TCN-VO$_2$ A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (-22 ml.min$^{-1}$, [-262, 218]), spanning transitions from low-moderate (-23 ml.min$^{-1}$, [-250, 204]), low-high (14 ml.min$^{-1}$, [-252, 280]), ventilatory threshold-high (-49 ml.min$^{-1}$, [-274, 176]), and maximal (-32 ml.min$^{-1}$, [-261, 197]) exercise. Second-by-second classification of physical activity across 16090 s of predicted VO$_2$ was able to discern between vigorous, moderate, and light activity with high accuracy (94.1%). This system enables quantitative aerobic activity monitoring in non-laboratory settings across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.