【摘要】：在社會生活中,受中風、交通事故等一些內(nèi)在或外在因素的影響,導致人體自由行動能力受到損傷,這對個人、家庭和社會造成了嚴重影響。為了幫助這些行動能力受損的群體恢復其獨立自主的生活能力,智能動作輔助機器人越來越受到重視,將人體表面肌電信號(Surface Electromyography,sEMG)與康復機器人相結(jié)合而得到的康復輔助系統(tǒng),在保留人體主觀性和靈活性的前提下,通過增強人體的現(xiàn)有運動能力,能夠有效改善或解決這一問題,在康復、醫(yī)療等領(lǐng)域有著廣泛的研究和應用。本文以人體關(guān)節(jié)運動的動力部分——骨骼肌為對象,在分析其表面肌電信號與肌力關(guān)系的基礎(chǔ)上,主要研究了不同收縮形式下的肌力預測方法和肌疲勞補償策略,并結(jié)合六自由度下肢康復訓練機器人,對肌力預測結(jié)果的準確性和實用性進行了驗證。本文的主要工作包括:(1)分析了骨骼肌收縮的電生理過程以及肌電信號-肌力關(guān)系,重點研究了關(guān)節(jié)角度和肌疲勞程度對肌電信號-肌力關(guān)系的影響。在此基礎(chǔ)上設(shè)計了信號采集的實驗方案,針對所采集表面肌電信號與肌力信息之間的時間延遲問題,采用極值定位方法進行同步處理。(2)基于支持向量回歸(Support Vector Regression,SVR)的非模型法進行肌力預測研究。針對骨骼肌收縮的力特性,將肌肉收縮模式分為靜力收縮和動力收縮兩種模式,研究不同收縮模式下的肌力預測方法,運用遺傳算法對模型當中的參數(shù)進行優(yōu)化選擇。(3)研究了肌力預測過程中的肌疲勞補償策略,在實驗的基礎(chǔ)上,分析了肌疲勞的表征參數(shù)與肌力預測誤差的變化關(guān)系,對實際應用中的肌疲勞現(xiàn)象進行誤差補償,將肌力預測條件從非疲勞狀態(tài)擴展到疲勞狀態(tài),進一步提高肌力預測的實用性。(4)研究了肌力預測在康復機器人控制中的應用,結(jié)合設(shè)計的肌力預測軟件,將肌力預測結(jié)果用于康復機器人平臺的力-速度位移控制,對系統(tǒng)的穩(wěn)定性進行了驗證。本文針對表面肌電信號與肌肉作用力之間的復雜關(guān)系設(shè)計了肌力預測的信號采集實驗方案,對信號進行了同步處理,基于肌肉活動程度函數(shù)采用支持向量回歸的方法完成了人體下肢末端作用力的預測,設(shè)計實現(xiàn)了一套肌力預測軟件系統(tǒng)并完成了下肢康復機器人平臺的力-速度位移控制實驗。
[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.
【學位授予單位】：武漢理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R49;TN911.7

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本文編號：2187593