基于邊際譜熵的肌肉疲勞實時評估方法研究
[Abstract]:Muscle fatigue is a complex physiological phenomenon. Aiming at the problem of real-time evaluation of muscle fatigue by surface electromyography (EMG) signals, which requires both fast, reliable and anti-noise indexes, a real-time evaluation method of muscle fatigue based on marginal spectrum entropy is proposed. Firstly, using deterministic periodic signals with different data lengths and Gao Si white noise, the paper analyzes the marginal spectral entropy rapidity and data length robustness. Using the muscle fatigue signal of extensor Carpi radialis longus under the condition of continuous static contraction of grip force from 100%MVC to 50%MVC, the marginal spectrum entropy was analyzed to evaluate the reliability of muscle fatigue and the stability of muscle applied to different individuals. The noise resistance of marginal spectral entropy was investigated by adding Gao Si white noise and electrocardiogram noise to the muscle fatigue signal. The experimental results show that the marginal spectral entropy is faster than the approximate entropy and the median frequency, the data length is more robust, the linear fit is better (0.46 鹵0.14), the slope coefficient of variation is lower (30.30%), and the slope coefficient of variation is higher for different individuals. When Gao Si white noise and ECG noise were added, the variation rate of marginal spectral entropy goodness of fit was lower (34.39% and 3.78%, respectively), and had good noise resistance. Therefore, the marginal spectral entropy has the advantages of fast, reliable evaluation of muscle fatigue and anti-noise, which provides a new method for real-time evaluation of muscle fatigue.
【作者單位】: 合肥工業(yè)大學(xué)智能制造技術(shù)研究院;合肥工業(yè)大學(xué)機(jī)械工程學(xué)院;
【基金】:科技型中小企業(yè)技術(shù)創(chuàng)新基金(11C26213402042)項目資助
【分類號】:R318;TN911.7
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