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Comparative study on evaluation algorithms for neck muscle fatigue based on surface electromyography signal

杜云霄, 王殊轶, NdaroNyakuru Zaphlene, 左艳
- , 2018, DOI: 10.7507/1001-5515.201706014
Abstract: 本研究旨在客观比较颈部肌肉疲劳评价算法的差异性,找出更加有效的颈部肌肉疲劳评价算法,为伏案姿势下颈部肌肉疲劳提供人因工程定量评价方法。本文利用无线生理仪采集了 15 名受试者使用记忆枕伏案 12 min 的颈部胸锁乳突肌的表面肌电信号,使用平均功率频率、谱矩比、离散小波变换、模糊近似熵以及复杂度 5 个算法计算出相应的肌肉疲劳指标;并使用最小二乘法对肌肉疲劳指标进行线性回归得出确定系数 R2 与斜率 k;确定系数 R2 可评价各种算法的抗干扰性;对斜率 k 进行柯尔莫哥洛夫—斯米洛夫检验得到最大垂直距离 Lmax,Lmax 可评价各种算法对疲劳程度的区分能力。统计结果表明,在抗干扰方面,模糊近似熵在不同高度的记忆枕下都具有最大的 R2,且模糊近似熵与平均功率频率、离散小波变换的差异具有统计学意义(P < 0.05);在区分疲劳程度方面,模糊近似熵仍具有最大的 Lmax,最大值达 0.496 7。本文研究结果表明,模糊近似熵无论是在抗干扰性还是疲劳程度的区分能力上都优于其他算法,因此在进行颈部肌肉疲劳评价时,我们建议可将模糊近似熵作为一个较好的评价指标。
The purpose of this study is to compare the differences among neck muscle fatigue evaluation algorithms and to find a more effective algorithm which can provide a human factor quantitative evaluation method for neck muscle fatigue during bending over the desk. We collected surface electromyography signal of sternocleidomastoid muscle of 15 subjects using wireless physiotherapy Bio-Radio when they bent over the desk using memory pillows for 12 minutes. Five algorithms including mean power frequency, spectral moments ratio, discrete wavelet transform, fuzzy approximation entropy and the complexity algorithms were used to calculate the corresponding muscle fatigue index. The least squares method was used to calculate the corresponding coefficient of determination R2 and slope k of the linear regression of the muscle fatigue metric. The coefficient of determination R2 evaluates anti-interference ability of algorithms. The maximum vertical distance Lmax which is obtained by the Kolmogorov-Smirnov test for the slopes k evaluates the ability to distinguish fatigue of algorithms. The results indicate that in the aspect of anti-interference ability, the fuzzy approximation entropy has the largest R2 when using memory pillows with different heights. When the fuzzy approximate entropy is compared with average power frequency or the discrete wavelet transform, the differences are significant (P < 0.05). In terms of distinguishing the degree of fatigue, the approximate entropy is still the largest, with a maximum of 0.496 7. Fuzzy approximation entropy is superior to other algorithms in ability of anti-interference and distinguishing fatigue. Therefore, fuzzy approximation entropy can be used as a better evaluation algorithm in the evaluation of cervical muscle fatigue.
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