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Four channels of surface EMG signals were collected from six gesture movements, and their time-domain and timefrequency domain features were extracted. Principal component analysis is used to reduce the dimension of features. For the dimension-reduced features, the decision tree is used to build a gesture recognition model. The experimental results show that the overall accuracy of the model for classifying six kinds of actions is more than 97%, which has good application value.

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