【摘要】：全世界每年大約有1500萬人因腦卒中等心腦血管疾病,導致永久性的肢體癱瘓,這給患者日常生活帶來極大不便,也給家庭和社會帶來沉重的精神與經濟負擔。越來越多的腦卒中患者需要接受康復治療,以重獲肢體運動功能。目前,國內外研究者開發(fā)出多種康復機器人輔助腦卒中患者進行康復訓練�？祻蜋C器人的應用有望解決康復師人工訓練中存在的問題和緩解康復師資源緊張狀況。但是大多數康復機器人執(zhí)行機械式地輔助運動,不能有效重塑患者受損的神經通路,并且缺乏患者主動運動意圖,難以調動患者參與康復訓練的積極性;此外,康復訓練中缺乏對患者疼痛、疲勞等主觀感受的監(jiān)測,容易對患者造成二次傷害。針對這些問題,本文研究了基于患者疼痛感的康復機器人系統(tǒng)控制方法,通過檢測患者的生物反饋信號識別運動意圖并量化疼痛等級,以期建立有效安全的康復系統(tǒng)。首先,本文介紹了疼痛評估方法、腦-機接口(Brain-Computer Interface,BCI)技術、功能性電刺激(Functional Electrical Stimulation,FES)在康復機器人領域應用的國內外研究現狀,提出本文的主要研究內容和工作。其次,開展了基于多生理信號的疼痛強度識別方法研究。針對原始特征中含有大量無關或冗余的特征,導致疼痛強度識別率下降問題,設計基于遺傳算法的特征選擇技術,尋找與疼痛有關的特征組合,建立優(yōu)化的疼痛強度識別模型。再次,研究基于腦電信號識別患者主動意圖的方法。設計12HZ、15HZ、20HZ頻率的視覺刺激方案,提取相應的穩(wěn)態(tài)視覺誘發(fā)電位信號,通過信號處理過程,建立有效的腦-機接口,識別患者的主動意圖。然后,引入現代控制方法,研究FES應用于上肢康復訓練中的最優(yōu)控制策略。設計基于PD反饋的迭代學習控制算法,優(yōu)化FES的電刺激控制序列,完成軌跡跟蹤的康復任務,實現神經通路重塑和運動功能恢復。最后,進行基于疼痛反饋的腦-控康復系統(tǒng)的初步實驗研究。利用BCI辨識患者主動運動意圖作為上層康復指令,集成FES與康復機器人的康復系統(tǒng)執(zhí)行具體的康復訓練策略,監(jiān)測的疼痛信息作為反饋參數,調節(jié)康復訓練。
[Abstract]:Every year, about 15 million people in the world suffer from stroke and other cardiovascular and cerebrovascular diseases, resulting in permanent paralysis of limbs, which brings great inconvenience to patients' daily life, and also brings heavy mental and economic burden to family and society. More and more stroke patients need rehabilitation to regain limb motor function. At present, researchers at home and abroad have developed a variety of rehabilitation robots to assist stroke patients for rehabilitation training. The application of rehabilitation robot is expected to solve the problems existing in the artificial training of rehabilitators and relieve the shortage of resources of rehabilitators. However, most rehabilitation robots perform mechanically assisted exercise, which can not effectively reshape the injured nerve pathway, and lack the initiative motion intention of the patients, which makes it difficult to motivate the patients to participate in the rehabilitation training. The lack of monitoring of subjective feelings such as pain and fatigue in rehabilitation training can easily cause secondary injury to patients. Aiming at these problems, this paper studies the control method of rehabilitation robot system based on patient's pain feeling. By detecting the patient's biofeedback signal, we can recognize the motion intention and quantify the pain grade in order to establish an effective and safe rehabilitation system. Firstly, this paper introduces the methods of pain assessment, Brain-Computer interface (BCI) technology, and the application of functional electrical stimulation (Functional Electrical stimulation) in the field of rehabilitation robot, and puts forward the main research content and work of this paper. Secondly, the method of pain intensity recognition based on multiple physiological signals is studied. Aiming at the problem of reducing the recognition rate of pain intensity caused by a large number of unrelated or redundant features in the original features, a feature selection technique based on genetic algorithm is designed to search for the combination of features related to pain, and an optimized pain intensity recognition model is established. Thirdly, the method of recognizing patient's active intention based on EEG signal is studied. A visual stimulation scheme with 12HZ ~ 15HZ ~ 20HZ frequency was designed to extract the corresponding steady-state visual evoked potential (VEP) signal. Through signal processing, an effective brain-computer interface was established to recognize the active intention of the patient. Then, the optimal control strategy of FES in upper limb rehabilitation training is studied by introducing modern control method. An iterative learning control algorithm based on PD feedback was designed to optimize the electrical stimulation control sequence of FES to complete the rehabilitation task of track tracking and to achieve neural pathway remodeling and motor function recovery. Finally, a preliminary experimental study of brain-controlled rehabilitation system based on pain feedback was carried out. The active motion intention of patients was identified by BCI as the upper rehabilitation instruction, the rehabilitation system of FES and rehabilitation robot was integrated to carry out specific rehabilitation training strategy, and the monitored pain information was used as feedback parameter to adjust rehabilitation training.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R49;TP242

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本文編號：2149749