徐国政,宋爱国,高翔,陈盛,徐宝国.基于焦虑情绪与混杂控制的机器人辅助临床康复实验[J].仪器仪表学报,2017,38(10):2364-2372
基于焦虑情绪与混杂控制的机器人辅助临床康复实验
Robot assisted clinical rehabilitation experiment based on anxiety emotion and hybrid control
  
DOI:
中文关键词:  康复机器人  焦虑情绪  支持向量机  混杂控制  临床实验
英文关键词:rehabilitation robot  anxiety emotion  support vector machine  hybrid theory  clinical experiment
基金项目:国家自然科学基金 (61673114,61305095)、江苏省重点研发计划 (BE2015701)、江苏省自然科学基金 (BK20141426、BK20170898)、江苏省“青蓝工程”项目(QL00516014)资助
作者单位
徐国政 南京邮电大学 机器人信息感知与控制研究所南京210023 
宋爱国 东南大学仪器科学与工程学院南京210096 
高翔 南京邮电大学 机器人信息感知与控制研究所南京210023 
陈盛 南京邮电大学 机器人信息感知与控制研究所南京210023 
徐宝国 东南大学仪器科学与工程学院南京210096 
AuthorInstitution
Xu Guozheng Robotics Information Sensing and Control Institute, Nanjing University of Posts and Telecommunications, Nanjing 210023, China 
Song Aiguo College of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 
Gao Xiang Robotics Information Sensing and Control Institute, Nanjing University of Posts and Telecommunications, Nanjing 210023, China 
Chen Sheng Robotics Information Sensing and Control Institute, Nanjing University of Posts and Telecommunications, Nanjing 210023, China 
Xu Baoguo College of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 
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中文摘要:
      现有基于患者主动参与的机器人辅助康复训练方法,大多以感知患者“运动”参与为主,而忽略了“心理”层次主动参与,且人机交互控制方法未能同时将机器人连续变量运动控制或医师离散事件决策控制这种混杂特性融于统一框架内,具有一定的局限。针对此问题,提出一种基于焦虑情绪识别与混杂控制的机器人辅助临床康复控制方法。该方法首先分析机器人辅助康复过程中不同强度焦虑情绪生理响应特征的显著性及差异性;其次,运用基于径向基核的支持向量机设计焦虑情绪分类器;再次,基于混杂控制理论设计与患者焦虑情绪强度相一致的人机交互控制器;最后,选取3例脑卒中患者,运用Barrett公司WAMTM机器人构建临床康复实验平台,验证所提方法有效性。
英文摘要:
      In the current robot aided rehabilitation training methods based on patient’s active participation, the perception of the patients’ active participation are mostly from the viewpoint of “motor” while neglecting the “psychological” involvement. Moreover, the interactive control methods do not incorporate robotic continuous variable motion control and therapist discrete event decision control into a unified framework. To solve this problem, a robot assisted clinical rehabilitation training method is proposed based on anxiety emotion recognition and hybrid theory. Firstly, the significance and difference of anxiety with different arousal are analyzed; Secondly, an anxiety emotion classifier using radial basis function based support vector machine is developed. Besides, human robot interactive controller coinciding with the arousal of the patient’s anxiety is developed by using hybrid control theory. Finally, the proposed method is verified with three recruited stroke patients on a constructed clinical experimental platform featuring a Barrett WAMTM manipulator.
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