An LSTM-Attention-based Method to Muscle Fatigue Detection by Integrating Multi-Source sEMG Signals

Muscle fatigue detection can be of good help to many tasks such as athletes' physical training and soldiers' body status monitoring. Surface elecrtromyography (sEMG) signals are widely used in muscle fatigue detection. However, sEMG signals exist only when the muscle contracts and disappear when it relaxes, making muscle fatigue detection methods cannot work well in realistic applications. To solve this problem, a method based on phase space reconstruction is proposed to automatically filter useless signals and retain useful ones from raw sensor data, improving the practicality of the detection methods. In previous works on muscle fatigue detection, most researchers took only sEMG signals of the target muscle into consideration. However, in reality, when someone is doing physical work, several cooperative muscles rather than some single one participate in the task. Therefore, the exercise status of one muscle not only resides in its own sEMG signals, but also is included in its partners'. For this reason, a fatigue detection method to muscle fatigue detection based on integrating multi-source sEMG signals is proposed, where long short-term memories (LSTM) and one attention layer are used as an inference model. Moreover, a series of sequential detection results are integrated to make a final result to deal with accidental wrong judgements, which further improves the practicality. In our experiments, our LSTM-Attention-based method achieves an detection accuracy of 90.4%, which is much better than the method based on LSTM processing sEMG signals only from the target muscle.