生物医学工程学杂志

生物医学工程学杂志

基于表面肌电信号的颈部肌肉疲劳评价算法比较研究

查看全文

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 LmaxLmax 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.

关键词: 颈部肌肉疲劳评价; 模糊近似熵; 谱矩比; 离散小波变换; 人因工程

Key words: neck muscle fatigue evaluation; fuzzy approximation entropy; spectral moments ratio; discreate wavelet transform; human factor

引用本文: 杜云霄, 王殊轶, NdaroNyakuru Zaphlene, 左艳. 基于表面肌电信号的颈部肌肉疲劳评价算法比较研究. 生物医学工程学杂志, 2018, 35(1): 31-37. doi: 10.7507/1001-5515.201706014 复制

登录后 ,请手动点击刷新查看全文内容。 没有账号,
登录后 ,请手动点击刷新查看图表内容。 没有账号,
1. 刘世忠, 梁军, 林乾, 等. 伏案工作人群职业性颈肩肌肉疼痛运动处方的效果. 中国运动医学杂志, 2015, 34(7): 642-648.
2. Bronfort G, Evans R, Nelson B, et al. A randomized clinical trial of exercise and spinal manipulation for patients with chronic neck pain. Spine (Phila Pa 1976), 2001, 26(7): 788-797.
3. Cifrek M, Medved V, Tonkovic S, et al. Surface EMG based muscle fatigue evaluation in biomechanics. Clinical Biomechanics, 2009, 24(4): 327-340.
4. 陈谦, 马静, 王健. 疲劳负荷下颈部肌肉表面肌电活动的变化规律. 北京体育大学学报, 2010, 33(9): 52-55.
5. 包萨日娜, 朱守林, 戚春华, 等. 驾驶员颈部肌肉疲劳试验研究. 中国安全科学学报, 2014, 24(5): 68-72.
6. 王琳, 付荣荣, 张陈, 等. 基于生物力学分析Q值对颈肌疲劳的反映效果. 仪器仪表学报, 2017, 38(4): 878-885.
7. Chowdhury S K, Nimbarte A D, Jaridi M A. Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles. Journal of Electromyography and Kinesiology, 2013, 23(5): 995-1003.
8. Yang L F, Kang B. Study on human neck muscles'comfort of different height levels based on sEMG method//Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation. Atlantis Press, Paris, 2016: 563-574.
9. Lindstrom L, Magnusson R, Petersén I. Muscular fatigue and action potential conduction velocity changes studied with frequency analysis of EMG signals. Electromyography, 1970, 10(4): 341-356.
10. Merletti R, Knaflitz M, Luca C D. Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. J Appl Physiol, 1990, 69(5): 1810-1820.
11. Merletti R, Conte L L, Orizio C. Indices of muscle fatigue. Journal of Electromyography and Kinesiology, 1991, 1(1): 20-33.
12. Dimitrov G V, Todor A, Mileva K, et al. Muscle fatigue during dynamic contractions assessed by new spectral indices. Medecine and Science in Sports and Exercise, 2006, 38(11): 1971-1979.
13. Gonzalez-Izal M, Rodriguez-Carreno I, Malanda A , et al. sEMG wavelet-based indices predicts muscle power loss during dynamic contractions. Journal of Electromyography and Kinesiology, 2010, 20(6): 1097-1106.
14. Pincus S M. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A, 1991, 88(6): 2297-2301.
15. Xie Hongbo, Guo Jingyi, Zheng Yongping. Fuzzy approximate entropy analysis of chaotic and natural complex systems: detecting muscle fatigue using electromyography signals. Ann Biomed Eng, 2010, 38(4): 1483-1496.
16. Talebinejad M, Chan A C, Miri A. A lempel-Ziv complexity measure for muscle fatigue estimation. Journal of Electromyography and Kinesiology, 2011, 21(2): 236-241.
17. Gonzalez-Izal M, Malanda A, Gorostiaga E A. Electromyographic models to assess muscle fatigue. Journal of Electromyography and Kinesiology, 2012, 22(4): 501-512.
18. Kahl L, Hofmann U G. Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals. Med Eng Phys, 2016, 33(11):1260-1269.
19. Wang Jiachi, Chan R C, Wu Hanlin, et al. Effect of pillow size preference on extensor digitorum communis muscle strength and electromyographic activity during maximal contraction in healthy individuals: A pilot study. Journal of the Chinese Medical Association, 2015, 78(3): 182-187.