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Med."],"abstract":"Abstract<\/jats:title>Oxygen consumption ($$\\dot{\\,{{\\mbox{V}}}}{{{\\mbox{O}}}}_{2}$$<\/jats:tex-math>\n \n \n \n \n \n V<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n \u0307<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mover>\n \n \n \n O<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, $$\\dot{\\,{{\\mbox{V}}}}{{{\\mbox{O}}}}_{2}$$<\/jats:tex-math>\n \n \n \n \n \n V<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n \u0307<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mover>\n \n \n \n O<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of $$\\dot{\\,{{\\mbox{V}}}}{{{\\mbox{O}}}}_{2}$$<\/jats:tex-math>\n \n \n \n \n \n V<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n \u0307<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mover>\n \n \n \n O<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 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 $$\\dot{\\,{{\\mbox{V}}}}{{{\\mbox{O}}}}_{2}$$<\/jats:tex-math>\n \n \n \n \n \n V<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n \u0307<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mover>\n \n \n \n O<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 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 $$\\dot{\\,{{\\mbox{V}}}}{{{\\mbox{O}}}}_{2}$$<\/jats:tex-math>\n \n \n \n \n \n V<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n \u0307<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mover>\n \n \n \n O<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of $$\\dot{\\,{{\\mbox{V}}}}{{{\\mbox{O}}}}_{2}$$<\/jats:tex-math>\n \n \n \n \n \n V<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n \u0307<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mover>\n \n \n \n O<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218\u2009s history length (TCN-VO2 A), with 187, 97, and 76\u2009s yielding <3% deviation from the optimal validation loss. TCN-VO2 A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (\u221222 ml\u2009min\u2212<\/jats:sup>1<\/jats:sup>, [\u2212262, 218]), spanning transitions from low\u2013moderate (\u221223 ml\u2009min\u2212<\/jats:sup>1<\/jats:sup>, [\u2212250, 204]), low\u2013high (14 ml\u2009min\u2212<\/jats:sup>1<\/jats:sup>, [\u2212252, 280]), ventilatory threshold\u2013high (\u221249 ml\u2009min\u2212<\/jats:sup>1<\/jats:sup>, [\u2212274, 176]), and maximal (\u221232 ml\u2009min\u2212<\/jats:sup>1<\/jats:sup>, [\u2212261, 197]) exercise. Second-by-second classification of physical activity across 16,090\u2009s of predicted $$\\dot{\\,{{\\mbox{V}}}}{{{\\mbox{O}}}}_{2}$$<\/jats:tex-math>\n \n \n \n \n \n V<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n \u0307<\/mml:mo>\n <\/mml:mrow>\n <\/mml:mover>\n \n \n \n O<\/mml:mtext>\n <\/mml:mstyle>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 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.<\/jats:p>","DOI":"10.1038\/s41746-021-00531-3","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T11:06:09Z","timestamp":1636628769000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities"],"prefix":"10.1038","volume":"4","author":[{"given":"Robert","family":"Amelard","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9507-4533","authenticated-orcid":false,"given":"Eric T.","family":"Hedge","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6915-2959","authenticated-orcid":false,"given":"Richard L.","family":"Hughson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,11]]},"reference":[{"key":"531_CR1","doi-asserted-by":"publisher","first-page":"e653","DOI":"10.1161\/CIR.0000000000000461","volume":"134","author":"R Ross","year":"2016","unstructured":"Ross, R. et al. 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