Upper Limb Motion Recognition Method Based on PSO-LSTM Model

Aiming at the problem of poor recognition accuracy of upper limb muscle sound signal (MMG), A hybrid model (PSO-LSTM) based on Particle Swarm Optimization-Long Short Term Memory (PSO-LSTM) is proposed for action recognition. ADXL357, NI-9202 and other hardware were used to collect 5-channel upper limb muscle sound signals from healthy upper limb subjects, and then Butterworth Filter, Z-scores standardization and other methods were used to pre-process the muscle sound signals. The energy method of sliding window was used for effective action division and feature extraction. The LSTM neural network optimized by PSO algorithm was constructed, and the upper limb muscle sound signal recognition model was trained and tested on the PSO-LSTM model. Finally, we compared the Long Short Term Memory (LSTM) model and Sparrow Search Algorithm from different measures. Experimental results of Sparrow Search Algorithm-Long Short Term Memory (SSA), Sparrow Search Algorithm-Long Short Term Memory (SSA-LSTM) and PSO-LSTM. The results show that the accuracy and recall rate of the PSO-LSTM model are higher than those of the other two models, and the recognition accuracy of the test set is about 96.9%. In addition, the PSO-LSTM model is superior to the other two models in terms of iteration loss and iteration speed, which proves the superiority of this model for upper limb muscle sound signal recognition.