Hand shape recognition using the sEMG of the upper extremity
نویسندگان
چکیده
In this paper, we propose a hand shape recognition method using the sEMG (surface electromyography) of the upper extremity. The upper extremity sEMG is acquired using Myo which is a wearable device. The target hand shapes are three: Rock, Paper, and Scissors. For hand shape recognition, four features obtained with FFT and wavelet transform are used. SVM and an improved k-NN are used for classifiers. For hand shape recognition, the proposed method performs ensemble learning using these features and the classifiers. The ensemble learning to be used is AdaBoost.M1. As a result of the experiment, the recognition rate of Rock, Paper and Scissors are 86%, 81.3% and 75.6%, respectively.
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