A hybrid deep learning framework combined with intelligent garment system for real-time fatigue monitoring in cycling

Fatigue monitoring during sports activities is crucial for optimizing athletic performance and preventing injuries. However, existing methodologies frequently lack real-time capability and accuracy in dynamic sports environments. This study presents a hybrid deep learning framework that integrates Temporal Convolutional Networks with deep recurrent neural networks for real-time fatigue state recognition during cycling activities. The framework processes synchronized electrocardiography (ECG) and electromyography (EMG) signals through physiological signal fusion techniques acquired from an intelligent garment system. The EMG signals were recorded from both the Erector Spinae and Anterior Deltoid muscles, while ECG signals were captured via strategically positioned electrodes. In a comprehensive investigation involving 15 subjects, we conducted comparative analyses between our hybrid neural network architecture and four baseline deep learning models: TCN, Gated Recurrent Unit (GRU), Transformer, and recurrent neural networks. The subjects performed a structured cycling protocol alternating between low-intensity (5 km/h) and high-intensity (30 km/h) phases, with fatigue levels assessed utilizing the Borg Rating of Perceived Exertion scale. Experimental results demonstrate that our proposed hybrid model achieves superior performance with an average prediction accuracy of 89.20%, significantly outperforming traditional single-architecture approaches. The model’s efficacy was validated through multiple metrics, including precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). This study provides a viable approach to real-time fatigue monitoring in cycling sports, indicating potential value for future developments in sports science applications.