Effect of the sEMG electrode placement and feature set size on the hand movement recognition

Recording electrode array may not be exactly repositioned across repeated electromyography measurements resulting in a displacement error when aiming at hand movement classification. The influence of electrode displacement on classification accuracy and its relation to the feature set size is of interest for design of hand movement recognition system. In order to examine if the classifier re-training could reach satisfactory results when electrode array is translated along or rotated around subject's forearm for varying number of features, we recorded surface electromyography signals in 10 healthy volunteers for three types of grasp and six wrist movements. For feature extraction we applied principal component analysis and the feature set size varied from one to 8 principal components. Our results showed that there was no significant difference in classification accuracy when the array electrode was repositioned indicating successful classification re-training and optimal feature set selection. The results also indicate expectedly that the number of principal components plays a key role for acceptable classification accuracy ~90%. Interestingly, we showed that interaction between electrode array position and the feature set size is not statistically significant. This study emphasizes the importance of testing the interaction of factors that influence classification accuracy altogether with their impact independently in order to attain guiding principles for design of hand movement recognition system.