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

Abstract Oxygen consumption ( $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 ) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 monitoring is largely limited to specialized...

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Autores principales:	Robert Amelard, Eric T. Hedge, Richard L. Hughson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/5b7b3752512d45698685dc8c613a2949
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spelling	oai:doaj.org-article:5b7b3752512d45698685dc8c613a29492021-11-14T12:08:55ZTemporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities10.1038/s41746-021-00531-32398-6352https://doaj.org/article/5b7b3752512d45698685dc8c613a29492021-11-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00531-3https://doaj.org/toc/2398-6352Abstract Oxygen consumption ( $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 ) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 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 $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 . Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 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 16,090 s of predicted $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 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, when combined with tidal volume and heart rate reserve calibration, across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.Robert AmelardEric T. HedgeRichard L. HughsonNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Robert Amelard Eric T. Hedge Richard L. Hughson Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
description	Abstract Oxygen consumption ( $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 ) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 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 $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 . Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 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 16,090 s of predicted $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 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, when combined with tidal volume and heart rate reserve calibration, across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.
format	article
author	Robert Amelard Eric T. Hedge Richard L. Hughson
author_facet	Robert Amelard Eric T. Hedge Richard L. Hughson
author_sort	Robert Amelard
title	Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
title_short	Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
title_full	Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
title_fullStr	Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
title_full_unstemmed	Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
title_sort	temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5b7b3752512d45698685dc8c613a2949
work_keys_str_mv	AT robertamelard temporalconvolutionalnetworkspredictdynamicoxygenuptakeresponsefromwearablesensorsacrossexerciseintensities AT ericthedge temporalconvolutionalnetworkspredictdynamicoxygenuptakeresponsefromwearablesensorsacrossexerciseintensities AT richardlhughson temporalconvolutionalnetworkspredictdynamicoxygenuptakeresponsefromwearablesensorsacrossexerciseintensities
_version_	1718429404668887040

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

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