Myoelectric Control System Based on Wavelet Features

Electromyography (EMG) finds enormous applications in clinical/biomedical, prosthesis and rehabilitation devices. The main objective of this paper is to develop a cost-effective implementation of a prosthetic control system based on EMG signals. Non-invasive surface electrodes are used to acquire the signal for various actions. Since the signal is highly contaminated with noise, they are not used in its raw form to handle any sort of device. Amplification and filtering are therefore inevitable and becomes the foremost task prior to further processing so as to obtain a high-quality signal. After the conditioning of the signal, multi-level decomposition based on wavelet transform is performed and features are extracted from all the levels. They are then reduced to find the optimal performance. Finally, the selected features are able to distinguish between various hand movements and therefore helps in the recognition of the intended motion of the amputee.