Using the sEMG signal representativity improvement towards upper-limb movement classification reliability

Several Machine Learning techniques have been employed to process sEMG signals in order to provide a reliable control biosignal. Although some papers report accuracy rates superior to 90%, there is a lack of more detailed reasoning for reliable systems capable of providing control signals to users that may, for instance, control a prosthetic device. In this paper, we combined two strategies in order to increase the representativity of the sEMG...