一种人体运动机能非线性评价指标研究

Research on a Nonlinear Evaluation Index of Human Motor Function

穿戴式机器人在运动辅助过程中根据人体运动机能调整辅助模式/力度是实现人机协调共融的关键要素。通过对人体下肢正常步行时的表面肌电(sEMG)信号进行非线性分析,提出一种基于分形维数标准差的运动机能评价指标。首先,采用相空间重构和最大李雅普诺夫指数确定sEMG信号是混沌信号的基本属性;其次,由于混沌信号的自相似性,采用计算分形维数的方法表征与运动机能密切相关的sEMG信号的复杂度;最后提出分形维数标准差用于评价持续运动过程中肌肉的收缩-放松能力。通过对比分析运动机能存在差异的受试者的实验数据,证明了分形维数标准差与受试者个体的肌肉收缩-放松能力存在正相关性。实验结果表明,分形维数标准差非线性指标能够有效地反映人体运动机能。

The wearable robot adjusts the assist mode/strength according to the human body’s motor function during the exercise assisting process,which is a key element for achieving human-machine coordination and communism.Nonlinear analysis was performed on the surface electromyography(sEMG)signal of normal walking of human lower limbs.A motion function evaluation index based on standard deviation of classification dimension was proposed.Phase space reconstruction and maximum Lyapunov exponent were used to determine that the sEMG signal is the basic property of chaotic signals.Due to the self-similarity of chaotic signal,we choose the complexity of sEMG signal closely related to motor function by calculating the fractal dimension The standard deviation of fractal dimension was proposed to evaluate the muscle contraction and relaxation ability during continuous exercise.By comparing and analyzing the experimental data of the testers with different motor function,it is proved that there is a positive correlation between the standard deviation of the fractal dimension and the muscle contraction-relaxation ability of the tester’s individual.The experimental results show that the standard deviation of the standard deviation of fractal dimension can effectively reflect the human body’s motor function.