Classificação com SVM de sinais de EMG para implementação de próteses robóticas de baixo custo

People with amputation go through several challenges, many ofwhich are carried daily. The loss of functionality of the amputated limb can make some tasks difficult to perform, reducing their au-tonomy and quality of life. The use of robotic prostheses offers the possibility of returning elaborate movements that were previouslylost. Through sensors and motors, robotic prostheses are capableof performing user-controlled movements, allowing the partial orcomplete return of lost movements. This work presents the resultsobtained with a classifier trained with support vector machines forthe identification of movements through electromyography signals,obtained through non-invasive acquisition using surface electrodes.The results presented are part of several tests that will be carriedout for the development of a control system for low cost roboticprostheses. In this paper, an average of 85% accuracy was obtainedfor classifiers trained from 3 features extracted from the frequencydomain.