RealTime Chinese Sign Language Recognition Based on Artificial Neural Networks * Sign language is the main method for the deafmute to communicate with each other. However, normal people cannot understand the meaning of specific gestures without training. In this study, a realtime Chinese Sign Language (CSL) recognition model based on surface electromyographic (sEMG) signals and Artificial Neural Networks (ANN) is proposed. The model achieves an average accuracy at 88.7% on 15 CSL gestures with average response time around 300ms (timing at movement begins). In our approach, the MYO armband is used for acquiring raw sEMG signals. Signal preprocessing includes rectification, filter and extracting the muscle activation section. We apply a sliding window and a muscle detection approach to segment the signals and extract features. A test method is used to recognize the gesture when the totality of same label, which returned by ANN classifier, reach the activation times threshold.

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.