基于表面肌电信号的下肢肌力预测研究

发布时间：2018-08-17 12:13

【摘要】：在社会生活中,受中风、交通事故等一些内在或外在因素的影响,导致人体自由行动能力受到损伤,这对个人、家庭和社会造成了严重影响。为了帮助这些行动能力受损的群体恢复其独立自主的生活能力,智能动作辅助机器人越来越受到重视,将人体表面肌电信号(Surface Electromyography,sEMG)与康复机器人相结合而得到的康复辅助系统,在保留人体主观性和灵活性的前提下,通过增强人体的现有运动能力,能够有效改善或解决这一问题,在康复、医疗等领域有着广泛的研究和应用。本文以人体关节运动的动力部分——骨骼肌为对象,在分析其表面肌电信号与肌力关系的基础上,主要研究了不同收缩形式下的肌力预测方法和肌疲劳补偿策略,并结合六自由度下肢康复训练机器人,对肌力预测结果的准确性和实用性进行了验证。本文的主要工作包括:(1)分析了骨骼肌收缩的电生理过程以及肌电信号-肌力关系,重点研究了关节角度和肌疲劳程度对肌电信号-肌力关系的影响。在此基础上设计了信号采集的实验方案,针对所采集表面肌电信号与肌力信息之间的时间延迟问题,采用极值定位方法进行同步处理。(2)基于支持向量回归(Support Vector Regression,SVR)的非模型法进行肌力预测研究。针对骨骼肌收缩的力特性,将肌肉收缩模式分为静力收缩和动力收缩两种模式,研究不同收缩模式下的肌力预测方法,运用遗传算法对模型当中的参数进行优化选择。(3)研究了肌力预测过程中的肌疲劳补偿策略,在实验的基础上,分析了肌疲劳的表征参数与肌力预测误差的变化关系,对实际应用中的肌疲劳现象进行误差补偿,将肌力预测条件从非疲劳状态扩展到疲劳状态,进一步提高肌力预测的实用性。(4)研究了肌力预测在康复机器人控制中的应用,结合设计的肌力预测软件,将肌力预测结果用于康复机器人平台的力-速度位移控制,对系统的稳定性进行了验证。本文针对表面肌电信号与肌肉作用力之间的复杂关系设计了肌力预测的信号采集实验方案,对信号进行了同步处理,基于肌肉活动程度函数采用支持向量回归的方法完成了人体下肢末端作用力的预测,设计实现了一套肌力预测软件系统并完成了下肢康复机器人平台的力-速度位移控制实验。
[Abstract]:In social life, stroke, traffic accidents and other internal or external factors, lead to the damage of human freedom of movement, which has a serious impact on individuals, families and society. In order to help these groups with impaired mobility to recover their independent living ability, intelligent motion assistance robots are paid more and more attention to, and a rehabilitation assistance system is obtained by combining Surface electromyography (EMG) with rehabilitation robots. On the premise of retaining subjectivity and flexibility of human body, this problem can be effectively improved or solved by enhancing human body's present movement ability. It has been widely studied and applied in the fields of rehabilitation, medical treatment and so on. Based on the analysis of the relationship between the surface electromyography (EMG) signal and the muscle force, the muscle force prediction method and the muscle fatigue compensation strategy under different contractions are studied in this paper, which is the dynamic part of human joint motion. The accuracy and practicability of the prediction results of muscle strength are verified by using six degrees of freedom lower limb rehabilitation training robot. The main work of this paper is as follows: (1) the electrophysiological process of skeletal muscle contraction and the relationship between electromyography and muscle force are analyzed, and the effects of joint angle and fatigue degree on the relationship between myoelectric signal and muscle force are studied. On this basis, the experimental scheme of signal acquisition is designed, aiming at the time delay between the collected surface EMG signal and the muscle force information. (2) Non-model method based on support vector regression (Support Vector) is used to predict muscle strength. According to the force characteristic of skeletal muscle contraction, the muscle contraction mode is divided into static contraction mode and dynamic contraction mode, and the prediction methods of muscle force under different contraction modes are studied. Genetic algorithm is used to optimize the parameters of the model. (3) the strategy of muscle fatigue compensation in the process of muscle strength prediction is studied. Based on the experiments, the relationship between the parameters of muscle fatigue and the error of muscle force prediction is analyzed. In order to further improve the practicability of muscle force prediction, the application of muscle force prediction in rehabilitation robot control is studied by compensating the error of muscle fatigue phenomenon in practical application, extending the condition of muscle force prediction from non-fatigue state to fatigue state, and improving the practicability of muscle force prediction. Combined with the designed muscle force prediction software, the results of muscle force prediction are applied to the force-velocity displacement control of the rehabilitation robot platform, and the stability of the system is verified. In view of the complex relationship between surface EMG signal and muscle force, a signal acquisition scheme for muscle force prediction is designed in this paper, and the signal is processed synchronously. Based on the degree of muscle activity function, the support vector regression method is used to predict the end force of human lower extremity. A software system of muscle force prediction is designed and implemented, and the experiment of force-velocity displacement control on the platform of lower limb rehabilitation robot is completed.
【学位授予单位】：武汉理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：R49;TN911.7

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