Precise and Accurate Multifunctional Prosthesis Control Based on Fuzzy Logic Techniques

This paper proposes a fuzzy approach to classify single-channel surface electromyography (SEMG) signals for multifunctional prosthesis control. In this approach three variables root mean square, standard deviation & variance were selected for the analysis. These three parameters gave best results for discriminating hand movements from time domain analysis using SEMG. As the frequency range of SEMG signal is specified to be from 0 Hz to 400 Hz, therefore the analysis was divided into three sections of frequencies as: low(250 Hz) and band pass (70-250 Hz). This is done to establish the frequency change in the three regions for specified selected output. The three parameters were used as input variables to fuzzy logic controller for discrimination of the hand movements i.e. whether the hand is closed or open. SEMG signal was taken from below elbow position using bipolar electrodes as this part was found to be more active during expansion and contraction of muscles. Out of the three parameters standard deviation gave best results for discriminating hand movements (opening and closing). Out of three filters, low pass filter gave the best results for discriminating opening and closing movements. Further, to develop precise control of the grip prosthetic, the work is extended to multilevel SEMG control. A grip-exerciser was used to calibrate the executed force and SEMG signal. The result was a fuzzy logic controller for the accurate grip force.