Acquisition, processing of myoelectrics signals and Support-Vector Machine for movement characterization of hand-arm segment

The electrical engineering has been contributed with medical science to improve the knowledge and the practical applications in disabled ones life. In general, these researches aim to restore mobility and freedom to the disabled. This paper intends to develop a system that uses Surface Electromyography and Support-Vector Machines (SVM) for the characterization of specific movements of a human arm enabling the future integration in rehabilitation systems. At first, myoelectric signals are obtained in the arm muscles of a volunteer by means of surface electrodes attached to an Electromyography. The signal is acquired using a virtual model as pattern demonstrating to the volunteer the hand-arm movements which are to be replicated by the subject. The system acquires the myoelectric signals as the volunteer reproduces the visualized movements on a screen. After this, these signals are processed to extract their characteristics. Some of them (such as RMS, standard deviation, variance, mean, kurtosis, skewness) are the input data in the Support-Vector Machine. The output data is a valid or invalid movement recognition. At the end of the process the nine different movements reached an average hit rate of 79.4%.