摘要
目的利用采集的上斜方肌的肌音信号,研究肌音信号的非线性特征分形维数(fractal dimension,FD)趋势与肌肉疲劳的对应关系。方法在恒力做耸肩静态动作时,提取上斜方肌的肌音信号,采用一种自适应噪声的完备经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)算法去噪,选用平均功率频率(mean power frequency,MPF)和中值频率(median frequency MDF),提取肌音信号的特征值,并用非线性特征分形维数来表征疲劳程度。结果 CEEMDAN算法去噪效果明显,肌音信号的分形维数随肌肉疲劳程度的加深呈现近似线性下降的趋势,在肌肉疲劳评估中与MPF、MDF效果相似。结论肌音信号的分形维数特征可评估肌肉疲劳程度。
Objective To explore the relationship between the nonlinear characteristic fractal dimension(FD)trend and muscle fatigue.Methods The mechanomyography(MMG)signals of the upper trapezius muscle were collected when performing the static action of shoulder shrugging with constant force.A complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)was adopted.Mean power frequency(MPF)and median frequency(MDF)were selected,and FD was proposed to characterize the level of fatigue.Results The CEEMDAN algorithm was effective in denoising processing,and the fractal dimension of MMG signal presented an approximate linear decline with the deepening of muscle fatigue,which had a similar effect in evaluating muscle fatigue with the characteristic values of MPF and MDF.Conclusion Fractal dimension can provide a new basis for MMG signal to quantify muscle fatigue in the nonlinear field.
作者
蒋文都
夏春明
章悦
封万俊
Jiang Wendu;Xia Chunming;Zhang Yue;Feng Wanjun(School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai 200237,China)
出处
《航天医学与医学工程》
CAS
CSCD
北大核心
2019年第3期259-264,共6页
Space Medicine & Medical Engineering
