首页> 外文会议>International ISA biomedical sciences instrumentation symposium;International Society of Automation;Annual Rocky Mountain bioengineering symposium >DIFFERENTIATING MUSCLE FATIGUE AND NONFATIGUE CONDITIONS USING SURFACE EMG SIGNALS AND ZHAO-ATLAS-MARKS BASED TIMEFREQUENCY DISRIBUTION
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DIFFERENTIATING MUSCLE FATIGUE AND NONFATIGUE CONDITIONS USING SURFACE EMG SIGNALS AND ZHAO-ATLAS-MARKS BASED TIMEFREQUENCY DISRIBUTION

机译:使用表面肌电信号和基于赵阿特拉斯标记的时间分布区分肌肉疲劳和非疲劳状况

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Muscle fatigue is a neuromuscular condition where muscles fail to generate the required force. It occurs in normal as well asabnormal subjects. The analysis of muscle fatigue plays a significant role in the field of clinical studies, myo-electric control,ergonomics and sports biomechanics. In this work, an attempt has been made to differentiate the sEMG signals under musclenon-fatigue and fatigue conditions using Zhao-Atlas-Marks (ZAM) based time frequency distribution. For this purpose,sEMG signals are recorded from fifty healthy volunteers during isometric contractions under well defined protocol. Theacquired signals are preprocessed and subjected to ZAM based time-frequency analysis. The time-frequency based featuressuch as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted from the timefrequencyspectrum. The results show that IMDF and IMNF are distinct for muscle non-fatigue and fatigue conditions.Further, more number of frequency components are observed in the time-frequency spectrum of signals recorded innonfatigue conditions. The t-test performed on these features has shown significant difference (p<0.01) in between nonfatigueand fatigue conditions. Thus the study seems to be useful for the analysis of various neuromuscular conditions.
机译:肌肉疲劳是一种神经肌肉疾病,其中肌肉无法产生所需的力量。它在正常情况下以及 异常对象。肌肉疲劳分析在临床研究,肌电控制, 人体工程学和运动生物力学。在这项工作中,已尝试区分肌肉下的sEMG信号。 使用基于Zhao-Atlas-Marks(ZAM)的时间频率分布的非疲劳和疲劳状况。以此目的, sEMG信号是根据明确定义的方案在等距收缩过程中记录的五十名健康志愿者的信号。这 对采集到的信号进行预处理,并进行基于ZAM的时频分析。基于时频的功能 例如从时间频率中提取瞬时中值频率(IMDF)和瞬时平均频率(IMNF) 光谱。结果表明,IMDF和IMNF在肌肉非疲劳和疲劳条件下截然不同。 此外,在记录在其中的信号的时间频谱中观察到更多的频率分量。 非疲劳状况。对这些特征进行的t检验显示,非疲劳之间存在显着差异(p <0.01) 和疲劳状况。因此,该研究似乎对各种神经肌肉疾病的分析很有用。

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