A New Electrode System for Hand Action Discrimination
Sign languages are composed of hand and finger actions and are combinations of the flexion and extension of fingers, wrist, forearm, and arm. Thus, an EMG-based hand action identification system is proposed in this study. The purpose of this system is to identify a sufficient amount of basic hand actions in order to use this information to recognize more complicated sign language in the future. This system uses active electrodes placed around the forearm to collect EMG signals from muscle groups of the forearm. To avoid miss-identification of the action period due to noise and artifacts in the EMG signals, in this study a multi-thresholds method is proposed. Features extracted from the new EMG electrode system are inputted to a back-propagation ANN identification system for hand action discrimination. Eleven subjects were recruited for this study. The results indicate that when six features from seven EMGs were input into the ANN, the average discrimination rate was 93.1%. When one feature from each channel was used, the discrimination rates ranged from 73.2% to 90.4%. On the other hand, when two features with the highest discrimination rate in the previous results were selected, the average discriminative rate increased to 86.9% and 90.3%. However, the current system cannot detect movements of the upper arm. Additionally, due to the large between-subject variations, the system must go through the training sequence before every use. Nevertheless, the results indicate that, with the ring electrode system and multi-thresholds method, the proposed system does provide high discriminative ability for the actions of fingers, palm, wrist, and forearm.