Study on Discrimination of Finger Motions based on EMG signals In recent years, biological signals have attracted attention as tools for human interfaces. Researches on biological signals have been actively conducted. In this paper, we propose a method which distinguishes ten motions, such as xe2x80x9cOnexe2x80x9d xe2x80x9cTwoxe2x80x9d xe2x80x9cThreexe2x80x9d xe2x80x9cFourxe2x80x9d xe2x80x9cFivexe2x80x9d xe2x80x9cSixxe2x80x9d xe2x80x9cSevenxe2x80x9d xe2x80x9cEightxe2x80x9d xe2x80x9cNinexe2x80x9d and xe2x80x9cTenxe2x80x9d by measured the electromyogram of the wrist. We measure data by installing 8 dry type sensors on the right wrist. We carry out frequency analysis using FFT and try to take 3 kinds of methods to remove noise. Finally, we use Support Vector Machine (SVM) for identification and classification. We conducted experiments with four subjects. In the experimental result, the accuracy of finger motions recognition was 65%. In the future, we will also add more methods to remove noise, and try to find other methods to improve the accuracy in the research.

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.