Development of an Armband EMG Module and a Pattern Recognition Algorithm for the 5-Finger Myoelectric Hand Prosthesis

A robust algorithm to classify various hand postures using EMG signals is needed for the EMG-based electric hand prosthesis with the multiple degrees of freedom. In this study, an armband-type multi-channel EMG module was designed, and an algorithm for classifying seven different types of hand postures was developed using the artificial neural network (ANN). The classification accuracy was evaluated for ten normal volunteers, according to the independence of the EMG feature groups, donning and doffing training data size, and whether or not majority voting was applied. The results revealed an optimized accuracy of 97.49 ± 3.87% when majority voting was applied after using high independence feature group (HIFG) to perform classification training for seven or more sessions. The algorithm was successfully applied to provide seven different hand postures in a 5-finger myoelectric hand prosthesis. Confusion matrices and separability indexes of ANN classifiers showed that the major misclassifications, in spite of a good accuracy, were found to be lateral pinch versus palmar pinch, and index versus thumb-up. However, with the classification training for seven or more sessions, the probability of misclassification significantly decreased.