Key result
A CNN-LSTM model with a spatial attention mechanism achieved the highest accuracy for predicting exercise oxygen consumption (R2 = 0.962), outperforming baseline CNN-LSTM and plain LSTM models.
A CNN-LSTM model with spatial attention integrating accelerometer and heart-rate data accurately predicts dynamic oxygen uptake during exercise.
May enable wearable VO2 monitoring during exercise; leaves open prospective validation before clinical adoption.
Dynamic oxygen uptake (VO2) reflects moment-to-moment changes in oxygen consumption during exercise and underpins training design, performance enhancement, and clinical decision-making. We tackled two key obstacles—the limited fusion of heterogeneous sensor data and inadequate modeling of long-range temporal patterns—by integrating wearable accelerometer and heart-rate streams with a convolutional neural network–LSTM (CNN-LSTM) architecture and optional attention modules. Physiological signals and VO2 were recorded from 21 adults through resting assessment and cardiopulmonary exercise testing. The results showed that pairing accelerometer with heart-rate inputs improves prediction compared with considering the heart rate alone. The baseline CNN-LSTM reached R2 = 0.946, outperforming a plain LSTM (R2 = 0.926) thanks to stronger local spatio-temporal feature extraction. Introducing a spatial attention mechanism raised accuracy further (R2 = 0.962), whereas temporal attention reduced it (R2 = 0.930), indicating that attention success depends on how well the attended features align with exercise dynamics. Stacking both attentions (spatio-temporal) yielded R2 = 0.960, slightly below the value for spatial attention alone, implying that added complexity does not guarantee better performance. Across all models, prediction errors grew during high-intensity bouts, highlighting a bottleneck in capturing non-linear physiological responses under heavy load. These findings inform architecture selection for wearable metabolic monitoring and clarify when attention mechanisms add value.
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Wang et al. (2025) studied this question. Attention-Enhanced CNN-LSTM Model vs. Plain LSTM and baseline CNN-LSTM models was evaluated on Exercise oxygen consumption (VO2) prediction accuracy (R2). A CNN-LSTM model with a spatial attention mechanism achieved the highest accuracy for predicting exercise oxygen consumption (R2 = 0.962), outperforming baseline CNN-LSTM and plain LSTM models.
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