Classification of wrist movements through EMG signals with fuzzy logic algorithm

Electromyography (EMG) deals with measurement of electrical potential created by the activation of muscle fibers during voluntary or involuntary movements. As these signals are related to human process of motion, Due to uncertain nature of EMG signals the correct prediction of intended motion is quite difficult in any myoelectric control mechanism. A movement identification algorithm to distinguish various positions of movements of the upper limb is presented. Fuzzy stages were developed and if-then rules at each stage are related to the action of muscle fibers when performing a particular movement. This algorithm was evaluated using surface EMG measurements over the Flexion and Extension muscles.