Gait recognition based on the mean impact value and probability neural network

In order to effectively control the intelligent lower limb prosthesis, the effective identification of gaits (including walk, up and down stairs or hills, etc.) is the key. Firstly, this paper extracts characteristic values of different gait, uses Mean Impact Value to screen the values, and identifies six feature values in accordance with the characteristics of the knee amputees, for the maximum value, mean degree in Initial Stance, mean degree in Middle Stance, standard deviation in Middle Stance, standard deviation in Swing Phase of hip (Mh, ISh, MSh, SWh, MSV, SWV). Secondly, use the probabilistic neural network (PNN) to recognize, of which can accurately identify the five kinds of gaits in this experiment, and compare with BP neural network, the results show that the method, which uses the means of Mean Impact Value to screen eigenvalues and recognizes by Probabilistic Neural Network, has good recognition rate and recognition speed. The recognition rate which compared to BP neural network, improves by more than 10%. So this method is effective and feasibility. conveniently.