A discrimination system using neural network for EMG-controlled prostheses-Integral type of EMG signal processing

The electromyographic (EMG) signal from active muscle is one of the most effective signals for controlling externally powered upper extremity prostheses. However, the EMG signal depends on physical condition, the state of mind, and so on, so it is difficult to use the original EMG signal to control an externally powered upper extremity prosthesis directly. A discriminating system using an integral type of EMG signal processing and a neural network is proposed for such an application. The neural network is used to learn the relation between the integral values of the EMG signals and the performance desired by the handicapped person. It has been found that total discrimination time can become shorter than Fourier transform processing and the discrimination system can discriminate seven performances from the EMG signals with a probability of 95.5% using integral processing of the EMG signal