Beyond the Motion-Lab: Low-Cost Fatigue Monitoring in Cycling via Deep Learning

Fatigue detection during exercise currently relies on costly and specialized technologies, limiting its accessibility outside of controlled laboratory environments. This work explores a marker-less, low-cost alternative for detecting fatigue-related changes in cycling biomechanics using OpenPose, a deep convolutional neural network for human pose estimation, together with a custom supervised classification framework. We recorded sagittal-plane video of five participants performing a cycling ramp test under two conditions, Initial and Fatigue, yielding ten sessions at 29.97 fps. From the left-side keypoints we extracted four joint angle signals (knee, hip, trunk, and ankle), filtered with a 4th-order zero-phase Butterworth low-pass filter at 6 Hz. Three classifiers were compared under leave-one-subject-out cross-validation: an LSTM with attention, a plain LSTM, and XGBoost. Averaged across five random seeds, the plain LSTM achieved 65.4% mean accuracy, slightly outperforming the attention-based LSTM (63.4%), while XGBoost reached 52.3%. The attention model achieved the highest single-seed accuracy (78.7%), but at the cost of greater variance across initializations. The most consistent biomechanical finding was an increase in ankle range of motion under fatigue, consistent with a proximal-to-distal compensation strategy. These results constitute preliminary evidence that a marker-less pipeline based on OpenPose is a viable foundation for an accessible fatigue monitoring system in cycling.