中文摘要：

目的由于肌肉疲劳常与肌肉骨骼的功能失调有关，肌电信号可以反映肌肉作用力的信息，因此本文研究了一种利用某些频带上的能量特征，识别最大自主握力（maximum volunteer contraction，MVC）和疲劳状态下肌电信号的方法。方法实验记录10名年轻男子右上肢主动收缩时的表面肌电信号，并对表面肌电进行小波包变换得到第3层和第4层各节点的分解系数，由此计算各节点相应频段能量并且归一化后作为特征向量，最后将特征向量分别通过BP神经网络和支持向量机两种分类器完成识别。结果用3块前臂肌肉的表面肌电信号，通过4层小波包变换和BP神经网络的分类器对疲劳和最大自主握力状态的识别效果最好，利用7倍交叉检验方法得到87．5％的正确率。结论基于小波包能量分析的肌肉疲劳识别方法可有效检测肌肉收缩的不同状态。

英文摘要：

Objective Muscle fatigue is commonly associated with the musculoskeletal disorder problem. Surface electromyography （SEMG） provides important muscle activation information of exerted forces. This paper proposes a method to discriminate SEMG in maximum volunteer contraction （MVC） and fatigue state with the feature of certain frequency＇ s corresponding energy. Methods We recorded the SEMG signals on the right upper limbs from ten young men. The decomposition coefficients of each node on level 3 and level 4 for SEMG were gained by wavelet packet transform. The corresponding band energy of each node was normalized as feature vectors. The feature vectors were entered into back propagation neural network and support vector machine to recognize muscle fatigue. Results The muscle fatigue and MVC could be identified by four-level wavelet packet transform and back propagation neural network. The accuracy reached 87.5% with 7- fold cross-validation. Conclusions Recognition with wavelet packet energy transform can effectively distinguish muscle fatigue from different states of muscle contraction.