| Autor: |
Hedge ET; Schlegel-UW Research Institute for Aging, Waterloo, Ontario, Canada.; Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada., Amelard R; Schlegel-UW Research Institute for Aging, Waterloo, Ontario, Canada., Hughson RL; Schlegel-UW Research Institute for Aging, Waterloo, Ontario, Canada. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of applied physiology (Bethesda, Md. : 1985) [J Appl Physiol (1985)] 2023 Jun 01; Vol. 134 (6), pp. 1530-1536. Date of Electronic Publication: 2023 May 18. |
| DOI: |
10.1152/japplphysiol.00148.2023 |
| Abstrakt: |
Nonintrusive estimation of oxygen uptake (V̇o 2 ) is possible with wearable sensor technology and artificial intelligence. V̇o 2 kinetics have been accurately predicted during moderate exercise using easy-to-obtain sensor inputs. However, V̇o 2 prediction algorithms for higher-intensity exercise with inherent nonlinearities are still being refined. The purpose of this investigation was to test if a machine learning model can accurately predict dynamic V̇o 2 across exercise intensities, including slower V̇O 2 kinetics normally observed during heavy- compared with moderate-intensity exercise. Fifteen young healthy adults (seven females; peak V̇o 2 : 42 ± 5 mL·min -1 ·kg -1 ) performed three different pseudorandom binary sequence (PRBS) exercise tests ranging in intensity from low-to-moderate, low-to-heavy, and ventilatory threshold-to-heavy work rates. A temporal convolutional network was trained to predict instantaneous V̇o 2 , with model inputs including heart rate, percent heart rate reserve, estimated minute ventilation, breathing frequency, and work rate. Frequency domain analyses between V̇o 2 and work rate were used to evaluate measured and predicted V̇o 2 kinetics. Predicted V̇o 2 had low bias (-0.017 L·min -1 , 95% limits of agreement: [-0.289, 0.254]), and was very strongly correlated ( r rm = 0.974, P < 0.001) with the measured V̇o 2 . The extracted indicator of kinetics, mean normalized gain (MNG), was not different between predicted and measured V̇o 2 responses (main effect: P = 0.374, η p 2 = 0.01), and decreased with increasing exercise intensity (main effect: P < 0.001, η p 2 = 0.64). Predicted and measured V̇o 2 kinetics indicators were moderately correlated across repeated measurements (MNG: r rm = 0.680, P < 0.001). Therefore, the temporal convolutional network accurately predicted slower V̇o 2 kinetics with increasing exercise intensity, enabling nonintrusive monitoring of cardiorespiratory dynamics across moderate- and heavy-exercise intensities. NEW & NOTEWORTHY 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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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