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

ABSTRACT: Oxygen consumption ([Formula: see text]) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, [Formula: see text] monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] dynamics. A TCN mo

SUBMITTER: Amelard R 

PROVIDER: S-EPMC8586225 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature