基于生物信息感知的双侧镜像康复机器人控制方法研究

项目名称： 基于生物信息感知的双侧镜像康复机器人控制方法研究

项目编号： No.61273355

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 赵新刚

作者单位： 中国科学院沈阳自动化研究所

项目金额： 83万元

中文摘要： 我国2/3的脑卒中患者有一侧上肢功能障碍，如何有效促进患者上肢功能恢复,是康复领域的重要课题。现有上肢康复机器人基本是基于CPM原理，进行单侧训练，未考虑健侧运动的影响，患者主动参与度低。人们日常生活中的绝大多数活动是由双手协调来完成的,肢体同时活动时,各效应器之间会有同步化趋势,如肢体频率和相位的同步，说明镜向性对称运动是人体最典型的运动模式。本项目针对双侧镜像康复过程中的生理信息感知、机器人建模、机器人关节柔顺性控制等方面的问题开展研究，提出基于肌肉生理结构与肌电特征融合的肌电信号建模方法，基于肌电和心率变化的疼痛感分析方法，以及基于参数估计的人机一体化主动建模方法。以上述理论方法为基础构成基于生物感知的患者主动控制单元技术和基于疼痛感反馈的康复机器人安全控制单元技术，进一步构建基于双侧镜像运动疗法的康复机器人系统。为实现"人人享有康复服务"的国家战略目标提供理论方法基础和技术支撑。

中文关键词： 生物电感知；镜像康复；表面肌电信号；康复机器人；疼痛感检测

英文摘要： It is reported that about 2/3 of Chinese apoplexies are disenable in their upper limb activity. How to help these rehabilitants becomes a fundamental and urgent work for the researchers. The existing rehabilitation robot systems for upper limb mainly work on the CPM principle, but only consider training a single side instead of the other healthy one. Moreover, patients have seldom chances to participate in the rehabilitation. Most of human activities depend on the cooperation of their hands. During these activities, all the effects show a synchronous trend, such as the synchronization of frequency and phase between limbs. This demonstrates that the mirror symmetrical movement is the most typical movement patterns.Through the study of physiological information perception, the modeling of the robot, the robot flexibility control, and three issues of safety, comfort and effectiveness that rehabilitation robots faced will partly solved. This project proposed the sEMG signal modeling approach based on muscle physiology and EMG characteristics, sEMG based pain analysis methods, and parameter estimation based human-robot integration initiative modeling method. Base on the above theoretical approach, biological perception based patient's active control unit technology and pain feedback-based rehabilitation robot safety

英文关键词： Bioelectrical perception；mirror rehabilitation；Surface EMG signal；rehabilitation robot；pain detection