A flexible approach for segregating physiological signals

The interpretation of Surface Electromyogram (SEMG) signals at multiple muscle points for different operations of arm was investigated. Myoelectric signals were detected using designed acquisition setup consists of an instrumentation amplifier, filter circuit, an amplifier with gain adjustment. Further, Labview softscope code was used to record the SEMG signal for independent movements. The whole system consists of bipolar noninvasive electrodes, signal acquisition protocols and signal conditioning at different levels. Feature extraction was done for exercising Statistic Measured Index method to evaluate the distance between two independent groups by directly addressing the quality of signal. Thereafter factorial analysis of variance technique was investigated to analyze the effectiveness of recorded signal. Finally the SEMG feature evaluation index based reported work is a step forward to develop more powerful, flexible, efficient applications leading to prosthetic design.