RealTime Surface EMG Pattern Recognition for Hand Gestures Based on Support Vector Machine * In this paper, a realtime hand gesture recognition model based on surface electromyography signals is proposed. The Myo armband is applied to acquire surface electromyography (sEMG) signals generated by hand movement and we extract the features by using a sliding window to split the data. Then we select support vector machine (SVM) model for gesture recognition, which is trained by the training set and tested by testing set. We count the labels returned by classifier and assign the label whose times reach the threshold of recognition to the tested gesture. In order to evaluate this model, we set up an experiment with five target gestures which conducted by 12 subjects. Experimental results show that the average accuracy of proposed model can reach 97.8% and the recognition process is completed before the continuous action is finished, which means that this model can achieve realtime identification with high accuracy.

Recognition of Finger Motions Based on Surface Electromyographic Signals and Artificial Neural Network Surface electromyographic (sEMG) signals can be used in medicine, rehabilitation, robotics and industrial fields. This paper proposes a recognition method of finger motions for dexterous control of prosthetic hands. In this work, we record the corresponding sEMG signals from five subjects, and then set up the model by data preprocessing, feature extraction and classification. The results show that high recognition accuracy can be achieved by using time domain feature extraction and artificial neural network. It means that finger motions can be decoded accurately. To acquire the tradeoff between the number of channels and the recognition accuracy, we apply channel number reduction and it is found that the number of channels is at least 7 with recognition accuracy of 90.52%.