【作者】 邓晨曦；

【导师】 万柏坤；

【作者基本信息】 天津大学 ， 生物医学工程， 2009， 硕士

【摘要】 肌肉在持续收缩过程中会逐渐进入疲劳状态。如何有效地评价肌肉疲劳的程度,对于神经肌肉系统的基础研究、残疾人的康复工程、理疗效果的客观评价和运动员的科学训练等皆有重要的研究意义。表面肌电信号分析由于其方便及非侵入性,且可从整体角度用于分析肌肉的活动,是评价肌肉疲劳的有效工具。本文旨在通过分析肌肉在静动态收缩的过程中表面肌电特征参数的变化,研究肌电信号中的疲劳特征。研究中设计了静态和动态两种收缩条件下前臂肌肉疲劳的测试平台与实验方案。实验中受试者前臂肌肉握力维持一定水平或呈周期性变化至肌肉疲劳,同时采集其握力和屈肌表面肌电信号;对静态收缩下的表面肌电进行傅里叶变换,获得中值频率和平均频率指标;用连续小波变换分析两种收缩状态下的表面肌电:将小波系数分为高频带和低频带两个部分,以均方根值估计各频带肌电信号的幅值,用以分析在疲劳过程中这些参数的变化趋势。结果表明,静态收缩条件下肌肉疲劳过程中,中值频率和平均频率指标是疲劳的可靠参数。而小波分析则可用于静态和动态两种收缩状态疲劳肌电的分析。肌电的高频带幅值与肌力水平有关,而低频带幅值则表征了肌疲劳程度。本文经静、动态两种收缩条件下前臂肌肉疲劳实验和表面肌电信号谱分析研究得到了评价静动态肌肉疲劳的肌电信号特征,尤其是可通过高、低频带肌电信号幅值来分别估计肌力和肌疲劳,为动态肌肉收缩疲劳评价寻找到一种可行的分析方法。更多还原

【Abstract】 Muscle gets into fatigue during continuous contraction. How to estimate muscle fatigue effectively has an important significance in neuromuscular basic research, rehabilitation engineering, evaluation of the effect of physiotherapy and scientific training of athletes. Surface EMG analysis is an effective tool in muscle fatigue evaluation because that it is convenient, non-invasive, and can be used to analyze the activities of muscle overall. The purpose of this thesis is to analyze the characteristic parameters of sEMG during muscle static and dynamic contractions, and to study sEMG characteristics of muscle fatigue.In this thesis, forearm muscle fatigue experiments under static and dynamic contractions were performed, and a signal acquisition system was designed. For static contraction, subjects were requested to maintain the force level as steady as possible until exhausted. And in dynamic condition, subjects performed a cyclic and force varying dynamic contraction. The force data were measured from a handgrip dynamometer and sEMG data were recorded from flexor carpi ulnaris. sEMG from static contraction was analyzed with Fourier transform. The Median Frequency and Mean Frequency were calculated as characteristic parameters. SEMG from both contraction conditions were analyzed with Continuous Wavelet Transform using Matlab software. The wavelet coefficients were grouped into high frequency-band (65Hz-350Hz) and low frequency-band (5-45Hz). The amplitude of sEMG signal was determined by calculating the Root Mean Square.The results show that in static contraction condition, Median Frequency and Mean Frequency are reliable parameters of fatigue. Meanwhile, in both static and dynamic conditions, a correlation is discovered between amplitude of high frequency band and force level. On the other hand, the amplitude of low frequency band is associated with muscle fatigue.Through the forearm muscle fatigue experiments and spectral analysis of sEMG, the characteristic parameters which can estimate muscle fatigue are acquired. These results have an implication for estimating force and muscle fatigue simultaneously during dynamic contraction.更多还原