To solve the problem that the traditional back propagation(BP)neural network is easy to fall into local optimal solutions in gait phase recognition

and to enhance the accuracy and efficiency of BP neural network in gait phase recognition

a BP neural network recognition algorithm based on particle swarm optimization(PSO)is proposed.The algorithm adopts the data processed by filtering

feature extraction and segmentation based on gait phase division criteria as input

and the weights and thresholds of BP neural network are optimized by iteratively updating the velocity and position of particles.The input data are trained based on the optimized BP neural network

the trained model parameters are derived

and embedded into the exoskeleton prototype for real-time gait phase recognition test.The results show that the model has good real-time performance and high accuracy

and can accurately recognize the gait phase.