Leg amputees motion pattern recognition based on principal component analysis and BP network

The problem is the poor gait recognition accuracy for existing power-type prosthetic knee control. In order to improve the accuracy of the classification of prosthetic control system, the paper first analysel the signal acquisition of MMA7361L acceleration sensor and ENC-03 Gyro using the principal component analysis (PCA). It is used for the feature extraction, finally the BP neural network is used for training and testing. The experiment results show that this method can recognize lower limb prosthesis walking uphill, downhill, up and down stairs and different movement pattern recognition quickly and effectively.