基于生物电信号的上肢康复机器人的研究

发布时间：2018-10-24 11:54

【摘要】：随着近年来社会经济水平的不断提升,人们对生活质量的要求也越来越高,尤其是对医疗服务的需求。日益增加的瘫痪病人数量和突飞猛进的医疗科学技术共同促使了新型的康复训练、评价系统的产生。生物电信号(脑电信号、肌电信号等)介入到康复医学领域,为传统的康复疗法开辟了一条新的道路。脑电信号直接由瘫痪病人产生,其中包含着患者的主动意识,从中提取出患者的意识信息,便可以使其实现自主的运动控制,从而在康复训练的同时促使了患者大脑神经重塑。近十年发展起来的脑机接口技术为此提供了有力的技术支持。本文针对瘫痪病人,提出了一种自主式的上肢康复训练系统来帮助患者实现主动训练。本研究将脑机接口BCI(Brain-Computer-Interface)技术和机器人技术相结合,提出了整个系统的设计方案:将易于控制的情绪信号作为训练的开始触发和结束中断信号,而将对运动神经元重塑有益的运动想象脑电信号作为训练的动作执行信号。此外为了减轻患者运动想象的疲劳度,具体的动作执行由机器人系统负责实现,同时也提高了训练的效率和可靠性。本文提出了一种新颖的康复评价方法,肌电信号被用来对患者的康复状况进行评估。肌肉电信号能够客观地反映正常手臂(无痉挛)由肌肉所产生的力的大小,进而反映处于主导地位的运动神经元重塑的情况和运动神经通路的通畅程度。由于肌肉电信号为混沌信号,非线性特征明显,本文选用近似熵方法对其进行建模分析,并对肌电信号的近似熵和肌电强度组成的二维特征向量使用二次判别分析(QDA)法进行分类,得到了65%的正确率。本课题的研究为上肢康复医学领域提供了一种新的训练方式和评价方法,给相关学者提供了参考,有助于拓宽相关领域的研究思路。
[Abstract]:With the development of social economy in recent years, the demand for quality of life is higher and higher, especially the demand for medical service. The increasing number of paralyzed patients and the rapid development of medical science and technology promote new rehabilitation training and evaluation system. Bioelectrical signals (EEG, EMG, etc.) are involved in the field of rehabilitation medicine, which opens a new way for the traditional rehabilitation therapy. The EEG signal is produced directly by the paralytic patient, which contains the patient's active consciousness, from which the patient's consciousness information can be extracted, which can make the patient realize the independent motion control, thus promote the patient's cerebral nerve remodeling while the rehabilitation training. The brain computer interface technology developed in the last decade provides powerful technical support for this purpose. This paper presents an autonomous upper limb rehabilitation training system for paralyzed patients to help patients achieve active training. In this study, the brain-computer interface BCI (Brain-Computer-Interface) technology is combined with the robot technology, and the design scheme of the whole system is put forward: the easily controlled emotional signal is used as the starting trigger and the end interrupt signal of the training. The motor imagination EEG signal, which is beneficial to the remodeling of motor neurons, is used as the executive signal of the training action. In addition, in order to reduce the fatigue of the patient's motion imagination, the specific action execution is carried out by the robot system, and the efficiency and reliability of the training are also improved. In this paper, a novel rehabilitation evaluation method is proposed. EMG signals are used to evaluate the rehabilitation status of patients. EMG signals can objectively reflect the force produced by the normal arm (without spasms), and then reflect the remodeling of the dominant motor neurons and the patency of the motor nerve pathway. Because the EMG signal is chaotic and nonlinear, the approximate entropy method is used to model and analyze the EMG signal. The two-dimension eigenvector composed of approximate entropy and myoelectric intensity of EMG signal is classified by quadratic discriminant analysis (QDA), and the accuracy is 65%. The research of this topic provides a new training method and evaluation method for the field of upper limb rehabilitation medicine, provides a reference for relevant scholars, and helps to broaden the research ideas in related fields.
【学位授予单位】：天津理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP242

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