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首页> 外文会议>International Conference on Computational Science(ICCS 2006) pt.1; 20060528-31; Reading(GB) >The Study on the sEMG Signal Characteristics of Muscular Fatigue Based on the Hilbert-Huang Transform
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The Study on the sEMG Signal Characteristics of Muscular Fatigue Based on the Hilbert-Huang Transform

机译：基于Hilbert-Huang变换的肌肉疲劳的sEMG信号特征研究

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摘要

Muscular fatigue refers to temporary decline of maximal power ability or contractive ability for muscle movement system. The signal of surface electromyographic signal (sEMG) can reflect the changes of muscular fatigue at certain extent. In many years, the application of signal of sEMG on evaluation muscular fatigue mainly focus on two aspects of time and frequency respectively. The new method Hilbert-Huang Transform(HHT) has the powerful ability of analyzing nonlinear and non-stationary data in both time and frequency aspect together. The method has self-adaptive basis and is better for feature extraction as we can obtain the local and instantaneous frequency of the signals. In this paper, we chose an experiment of the static biceps data of twelve adult subjects under the maximal voluntary contraction (MVC) of 80%. The experimental results proved that this method as a new thinking has an obvious potential for the biomedical signal analysis.
机译：肌肉疲劳是指肌肉运动系统的最大力量或收缩能力的暂时下降。表面肌电信号（sEMG）可以在一定程度上反映肌肉疲劳的变化。多年来，sEMG信号在评估肌肉疲劳中的应用主要集中在时间和频率两个方面。新方法Hilbert-Huang变换（HHT）具有强大的能力，可以同时分析时间和频率方面的非线性和非平稳数据。该方法具有自适应的基础，并且由于我们可以获得信号的局部和瞬时频率，因此更适合于特征提取。在本文中，我们选择了在80％的最大自愿收缩（MVC）下对十二名成人受试者的静态二头肌数据进行的实验。实验结果证明，该方法作为一种新思路，对于生物医学信号分析具有明显的潜力。

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