Hand gesture recognition of sEMG based on modified Kohonen network

In order to improve the accuracy rate of surface EMG (sEMG) pattern recognition, a modified Kohonen self-organizing competitive network is presented in this paper. Kohonen network has a simple algorithm and short time for clustering. There we adjust the structure of this network, and turn it into a supervised learning network by adding an output layer, then optimize the initial weight. The integrate EMG and power spectral density ratio of sEMG as the input of modified Kohonen network to identify the five kinds of movement patterns: extension of thumb, extension of wrist, flexion of wrist, side flexion of wrist and extension of palm. Experiments show that, compared with the traditional Kohonen network, the modified neural network classifier has the higher classification ability.