Muscle Fatigue Identification Using a Time Frequency Deep Autoencoder

This paper proposes a novel unsupervised method for muscle fatigue recognition. It uses a convolutional autoencoder to extract time-frequency EMG features and clusters the features into fatigue and non-fatigue groups. Experimental results show that the proposed method is more effective in discriminating muscle fatigue compared to conventional approaches. In addition, the clusters provide an effective way to determine a threshold for identifying muscle fatigue.