摘要
旨在量化分析肘关节肌肉疲劳评价算法的性能,寻找一种高质量的肘关节肌肉疲劳评价算法,为肘关节屈曲运动提供一种更加有效的肌肉疲劳实时监测方法。通过采集12名测试对象在不同负载下肘关节进行屈曲运动的表面肌电信号,计算平均频率(MNF),频谱距(SMR),小波方法WIRM1551,模糊近似熵(fApEn)和递归量化分析(RQA%DET)的评价指标,考虑同一疲劳条件下的抗干扰性与不同疲劳条件下的区分疲劳程度的能力比较分析5种疲劳评价算法的性能。疲劳评价算法的抗干扰性由指标的线性回归方程确定系数R^(2)进行评价,区分疲劳程度的能力由线性回归方程斜率k的最大垂直距离L_(max)进行评价。统计分析表明,在抗干扰性方面,频谱距SMR相比其他评价算法具有最大的确定系数R^(2)均值,与MNF与RQA%DET的差异具有统计学意义(P<0.05);在区分疲劳程度的能力方面,频谱距SMR在不同负载下均具有最大的L_(max)均值,最大L_(max)均值分别为0.883、0.766、0.622。研究结果表明,频谱距SMR在抗干扰与疲劳程度的区分能力上都优于其他算法,因此在今后进行肘关节肌肉疲劳评价时,我们建议将频谱距SMR作为一个优先考虑的评价算法。
The purpose of this paper is to quantitatively analyze the performance of the elbow muscle fatigue evaluation algorithm,to find a high-quality elbow muscle fatigue evaluation algorithm,and to provide a more effective real-time muscle fatigue monitoring method for elbow flexion movement.The mean frequency,spectral moment ratio,wavelet method,fuzzy approximate entropy and recursive quantitative analysis were calculated by collecting the surface EMG signals of the elbow joints under different loads.The evaluation index of compares the performance of the five evaluation methods by considering the anti-interference under the same fatigue condition and the ability to distinguish the fatigue degree under different fatigue conditions.The anti-interference of the fatigue evaluation method is evaluated by the linear regression equation of the evaluation index to determine the coefficient R^(2),and the ability to distinguish the degree of fatigue is evaluated by the maximum vertical distance L_(max)of the slope k of the linear regression equation.Statistical analysis showed that SMR had the largest R^(2)mean value compared with other evaluation methods in terms of anti-interference,and the difference between MNF and RQA%DET was statistically significant(P<0.05);SMR in terms of ability to distinguish fatigue degree The maximum L_(max)average value is obtained under different loads,and the maximum L_(max)average values are 0.883,0.766,and 0.622,respectively.The results show that the SMR method is superior to other algorithms in distinguishing between anti-interference and fatigue.Therefore,when evaluating elbow muscle fatigue,we recommend SMR as a priority evaluation method.
作者
孟庆丰
陶庆
来全宝
胡玉舸
MENG Qing-feng;TAO Qing;LAI Quan-bao;HU Yu-ge(School of Mechanical Engineering,Xinjiang University,Xinjiang Urumqi 830047,China;Center for Post-Doctoral Studies of Mechanical Engineering,Xinjiang University,Xinjiang Urumqi 830047,China)
出处
《机械设计与制造》
北大核心
2023年第3期53-57,62,共6页
Machinery Design & Manufacture
基金
国家自然科学基金资助项目(51865056)
机械制造系统工程国家重点实验室开放基金(sklms2018006)。
