Prediction of oxygen uptake in dragon boat athletes using machine learning and multimodal physiological signals

This study developed and evaluated a machine learning model for predicting oxygen uptake (VO₂) in dragon boat athletes using wearable signals and compared performance across signal combinations and device groups.

Twelve male collegiate dragon boat athletes completed nine tests grouped into six task types (T1-T6): 200-, 500-, and 1000-m all-out paddling tests; an incremental test; a five-stage constant-load test; and four supramaximal-intensity tests pooled as T6. Heart rate, respiratory rate, device-derived minute ventilation, and muscle-oxygenation signals were collected. Fifteen signal combinations (G1-G15) and six device groups (A-F) were evaluated using a multilayer perceptron model. Performance was assessed using mean absolute error (MAE) and root mean square error (RMSE), with mean absolute percentage error (MAPE) also reported.

G13-G15 showed comparable performance, with no significant differences. G14 yielded the lowest MAE and MAPE (2.21 mL·kg^-1·min^-1 and 9.48%), whereas G13 yielded the lowest RMSE (3.19 mL·kg^-1·min^-1). No significant differences were observed among signal combinations within Groups A, C, D, E, or F. Group F showed the lowest MAPE but did not differ significantly from Group B, whereas Group C showed significantly higher MAPE than Groups B, D, E, and F. Performance did not differ significantly across exercise tasks. During the 1000-m test, predicted and measured VO₂ were strongly correlated for G15 (r = 0.88), with a mean prediction error of 0.44 mL·kg^-1·min^-1 and descriptive 95% limits of agreement from -6.32 to 7.20 mL·kg^-1·min^-1.

Cardiorespiratory signals provided useful information for wearable-based VO₂ prediction, whereas the contribution of muscle-oxygenation signals depended on the input configuration. G13-G15 should be regarded as comparably performing configurations, and bilateral muscle-oxygenation monitoring was not consistently superior to unilateral monitoring. Multimodal wearable signals show potential for non-invasive VO₂ monitoring in dragon boat athletes, although larger samples and independent validation are required.