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A Preliminary Analysis of the Various Reaching Pattern Classifications

Surface electromyographic (sEMG) signals contain rich motion information, which could be used for motion recognition and activities of daily living detection. The study analyzed the possibility of classifying the various reaching patterns in a horizontal plane based on the sEMG signals. Three tasks, classifying directions and distances simultaneously (Task I), recognizing the directions (Task II), and distances (Task III) respectively, were designed for the purpose. The sEMG signals were recorded from nine muscles of the upper limb. Two time-domain features and three classification algorithms were applied to recognize different reaching patterns. The influence of different feature combinations and muscle groups was compared. The result showed that the classification rate for three tasks is lower than 90% based on the extracted time-domain features, and Task III achieved the highest classification rate among three tasks comparing the other two tasks whichever algorithms or feature combinations were used. Besides, the results demonstrated that the classification rate was sensitive to algorithms and muscle groups. The findings illustrated the complexity of reaching movement, and a personalized procedure should be designed to subtly control assist devices for patient rehabilitation.