Modified adaptive resonance theory based control strategy for EMG operated prosthesis for below-elbow amputee

This paper presents a modified adaptive resonance theory (ART1)-based control strategy for a below-elbow (BE) prosthesis. The statistical parameters and histogram from two channels of an electromyogram (EMG) signal have been used as the feature space for the classification of four limb functions. The ART1 neural network (NN) has been used for the classification. ART1 has been modified to learn the patterns in supervised manner to suit the application. Further, the criteria for the modification of the stored pattern have been made bi-directional and the matching criteria have been designed for bit-by-bit matching. The major challenge of using ART1 is to decide on the value of the vigilance parameter, as the classification success is drastically affected by this parameter. The criteria have been evolved to get the optimal value of the vigilance parameter. It is concluded that the best value of vigilance parameter is that which provides the same tolerance in matching as the minimum bit distance between the stored patterns. This scheme has also been implemented on an 8031 microcontroller.