一种基于HHT和AR模型的手部运动模式识别方法<sup>*</sup>

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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 HilbertHuang transform (HHT) and ARmodel. According to the frequencycredit 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 motionstart and the motionend points. The motionstart and motionend points are decided by the instantaneous amplitude of the IMF with the largest frequencycredit. ARmodel of each IMF is built to extract the handmotion features. Finally, the motionfeature 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 handmotion patterns (namely, palmar dorsiflexion and flexion, hand opening and closing) with the correct rate up to 91%.

Key words： HilbertHuang Transform (HHT) AutoRegressive (AR) Model Support Vector Machine (SVM) Patterns Recognition

Received: 25 December 2006

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	Articles by authors
	LUO ZhiZeng
	MA WenJie
	MENG Ming

Cite this article:

LUO ZhiZeng,MA WenJie,MENG Ming. Pattern Recognition of Hand Motions Based on HHT and ARModel[J]. , 2008, 21(2): 227-222.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2008/V21/I2/227

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