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面向外骨骼的下肢多模态协同量化分析与康复评估方法 被引量:1

Multi-modal synergistic quantitative analysis and rehabilitation assessment of lower limbsforexoskeleton
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摘要 针对传统下肢康复量表评估方法费时、费力且难以在外骨骼康复训练中使用的问题,本文基于下肢外骨骼机器人训练提出了一种多模态协同信息融合的下肢步行能力定量评估方法。该方法通过引入定量的协同指标融合电生理和运动学层面信息,显著提高康复评估过程的效率和信度。首先,采集受试者穿戴外骨骼步行训练的下肢肌电和运动学数据。然后,基于肌肉协同理论,使用协同量化算法构造肌电和运动学的协同指标特征。最后,融合电生理和运动学层面信息,建立模态特征融合模型,输出下肢运动功能评分。试验结果表明,本文所构造的肌电、运动学协同特征与临床量表的相关系数分别为0.799和0.825。融合后的协同特征在K近邻(KNN)模型中的结果得到了更高的相关系数(r=0.921,P <0.01)。该方法可以根据评估结果修改外骨骼机器人的康复训练模式,为实现“人在环中”的评估—训练同步模式奠定了基础,也为下肢远程康复训练和评估提供了一个潜在的方法。 In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training,this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion.The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information.First,electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton.Then,based on muscle synergy theory,a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics.Finally,the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score.The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825,respectively.The results of the fused synergistic features in the K-nearest neighbor(KNN)model yielded higher correlation coefficients(r=0.921,P<0.01).This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results,which provides a basis for the synchronized assessment-training mode of"human in the loop"and provides a potential method for remote rehabilitation training and assessment of the lower extremity.
作者 钟旭 张弼 李纪桅 张亮 元香南 张鹏 赵新刚 ZHONG Xu;ZHANG Bi;LI Jiwei;ZHANG Liang;YUAN Xiangnan;ZHANG Peng;ZHAO Xingang(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110o16,P.R.China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110016,P.R.China;Medical Engineering Department,Afiliated Hospital of Yangzhou University,Yangzhou,Jiangsu 225003,P.R.China;Department of Rehabilitation,Liaoning Provincial People's Hospital,Shenyang 110067,P.R.China;Shengjing Hospital of China Medical University,Shenyang 110004,P.R.China)
出处 《生物医学工程学杂志》 EI CAS 北大核心 2023年第5期953-964,共12页 Journal of Biomedical Engineering
基金 国家重点研发计划项目课题(2022YFB4703203) 国家自然科学基金项目(62103406,U22A2067) 辽宁省应用基础研究计划项目(2022JH2/101300102)。
关键词 康复评估 肌肉协同 模态融合 机器学习 脑卒中 Rehabilitation assessment Muscle synergy Modal fusion Machine learning Stroke
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