  The aim of this work was to identify six basic movements of the hand using
two systems. Being an interdisciplinary topic, there has been conducted
studying in the anatomy of forearm muscles, biosignals, the method of
electromyography (EMG) and methods of pattern recognition. Moreover, the signal
contained enough noise and had to be analyzed, using EMD, to extract features
and to reduce its dimensionality, using RELIEF and PCA, to improve the success
rate of classification. The first part uses an EMG system of Delsys initially
for an individual and then for six people with the average successful
classification, for these six movements at rates of over 80%. The second part
involves the construction of an autonomous system EMG using an Arduino
microcontroller, EMG sensors and electrodes, which are arranged in an elastic
glove. Classification results in this case reached 75% of success.
