Abstract In order to recognize the hand motions based on the surface electromyogram (SEMG), a feature extraction algorithm is presented which is built by the combination of HilbertHuang transform (HHT) and ARmodel. According to the frequencycredit of each intrinsic mode function (IMF) after HHT, six intrinsic mode functions (IMFs) are selected. In the meantime, the rectangle window is built to cut motion signals of the six IMFs based on the motionstart and the motionend points. The motionstart and motionend points are decided by the instantaneous amplitude of the IMF with the largest frequencycredit. ARmodel of each IMF is built to extract the handmotion features. Finally, the motionfeature vector processed by principal component analysis (PCA) is input into the SVM classifier to recognize the hand motions. The experimental results indicate that the proposed method can discriminate the four handmotion patterns (namely, palmar dorsiflexion and flexion, hand opening and closing) with the correct rate up to 91%.
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