【摘要】：全世界每年大約有1500萬(wàn)人因腦卒中等心腦血管疾病,導(dǎo)致永久性的肢體癱瘓,這給患者日常生活帶來(lái)極大不便,也給家庭和社會(huì)帶來(lái)沉重的精神與經(jīng)濟(jì)負(fù)擔(dān)。越來(lái)越多的腦卒中患者需要接受康復(fù)治療,以重獲肢體運(yùn)動(dòng)功能。目前,國(guó)內(nèi)外研究者開(kāi)發(fā)出多種康復(fù)機(jī)器人輔助腦卒中患者進(jìn)行康復(fù)訓(xùn)練�？祻�(fù)機(jī)器人的應(yīng)用有望解決康復(fù)師人工訓(xùn)練中存在的問(wèn)題和緩解康復(fù)師資源緊張狀況。但是大多數(shù)康復(fù)機(jī)器人執(zhí)行機(jī)械式地輔助運(yùn)動(dòng),不能有效重塑患者受損的神經(jīng)通路,并且缺乏患者主動(dòng)運(yùn)動(dòng)意圖,難以調(diào)動(dòng)患者參與康復(fù)訓(xùn)練的積極性;此外,康復(fù)訓(xùn)練中缺乏對(duì)患者疼痛、疲勞等主觀感受的監(jiān)測(cè),容易對(duì)患者造成二次傷害。針對(duì)這些問(wèn)題,本文研究了基于患者疼痛感的康復(fù)機(jī)器人系統(tǒng)控制方法,通過(guò)檢測(cè)患者的生物反饋信號(hào)識(shí)別運(yùn)動(dòng)意圖并量化疼痛等級(jí),以期建立有效安全的康復(fù)系統(tǒng)。首先,本文介紹了疼痛評(píng)估方法、腦-機(jī)接口(Brain-Computer Interface,BCI)技術(shù)、功能性電刺激(Functional Electrical Stimulation,FES)在康復(fù)機(jī)器人領(lǐng)域應(yīng)用的國(guó)內(nèi)外研究現(xiàn)狀,提出本文的主要研究?jī)?nèi)容和工作。其次,開(kāi)展了基于多生理信號(hào)的疼痛強(qiáng)度識(shí)別方法研究。針對(duì)原始特征中含有大量無(wú)關(guān)或冗余的特征,導(dǎo)致疼痛強(qiáng)度識(shí)別率下降問(wèn)題,設(shè)計(jì)基于遺傳算法的特征選擇技術(shù),尋找與疼痛有關(guān)的特征組合,建立優(yōu)化的疼痛強(qiáng)度識(shí)別模型。再次,研究基于腦電信號(hào)識(shí)別患者主動(dòng)意圖的方法。設(shè)計(jì)12HZ、15HZ、20HZ頻率的視覺(jué)刺激方案,提取相應(yīng)的穩(wěn)態(tài)視覺(jué)誘發(fā)電位信號(hào),通過(guò)信號(hào)處理過(guò)程,建立有效的腦-機(jī)接口,識(shí)別患者的主動(dòng)意圖。然后,引入現(xiàn)代控制方法,研究FES應(yīng)用于上肢康復(fù)訓(xùn)練中的最優(yōu)控制策略。設(shè)計(jì)基于PD反饋的迭代學(xué)習(xí)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(fù)任務(wù),實(shí)現(xiàn)神經(jīng)通路重塑和運(yùn)動(dòng)功能恢復(fù)。最后,進(jìn)行基于疼痛反饋的腦-控康復(fù)系統(tǒng)的初步實(shí)驗(yàn)研究。利用BCI辨識(shí)患者主動(dòng)運(yùn)動(dòng)意圖作為上層康復(fù)指令,集成FES與康復(fù)機(jī)器人的康復(fù)系統(tǒng)執(zhí)行具體的康復(fù)訓(xùn)練策略,監(jiān)測(cè)的疼痛信息作為反饋參數(shù),調(diào)節(jié)康復(fù)訓(xùn)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(xué)位授予單位】：沈陽(yáng)理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：R49;TP242

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本文編號(hào)：2149749