Oxygen uptake (VO2) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate (HR) is a prominent physiological indicator correlated with VO2 and is often used for indirect VO2 prediction. This study investigates the impact of HR placement on VO2 prediction accuracy by analyzing HR data combined with the respiratory rate (RESP) and minute ventilation (VE) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on VO2, RESP, VE, and HR were collected and used to develop a neural network model for VO2 prediction. The influence of HR position on prediction accuracy was assessed via Bland-Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE). Our findings indicate that HR combined with RESP and VE VO2_HR+RESP+VE) produces the most accurate VO2 predictions (MAE: 165 mL/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of VO2 prediction decreases, particularly within high-intensity exercise. The substitution of HR with different anatomical sites significantly impacts VO2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering HR placement in VO2 prediction models, with RESP and VE serving as effective compensatory factors. These findings contribute to refining indirect VO2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements.
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