Physiological signal analysis in exercise fatigue detection application based on deep learning

This paper proposes a physiological signal analysis method in exercise fatigue detection application based on deep learning models to provide fast and accurate feedback for the player's physical status and better assist the player to perform exercise. We adopt the deep neural network as backbone model and design following strategies in our proposed method to process and extract features in signals. First, we preprocess the physiological signal, including noise reduction and segmentation. Second, we use a deep learning model to design a feature extraction method, which uses an autoencoder to label and feature the signal. Third, we perform motion fatigue detection on the fused signal features based on a long short-term memory network model. The results prove that the method proposed has good performance.