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 110016, P. R. China.
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Oct 25;40(5):953-964. doi: 10.7507/1001-5515.202212028.
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 -nearest neighbor (KNN) model yielded higher correlation coefficients ( = 0.921, < 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.
针对传统下肢康复量表评估方法在下肢外骨骼康复训练中存在耗时且使用困难的问题，本文提出一种基于多模态协同信息融合的下肢外骨骼机器人训练的下肢行走能力定量评估方法。该方法通过引入融合电生理和运动学水平信息的定量协同指标，显著提高了康复评估过程的效率和可靠性。首先，从穿着外骨骼进行行走训练的受试者身上采集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建肌电和运动学的协同指标特征。最后，融合电生理和运动学水平信息，建立模态特征融合模型并输出下肢运动功能评分。实验结果表明，构建的肌电和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）模型中的结果产生了更高的相关系数（ = 0.921， < 0.01）。该方法可根据评估结果调整外骨骼机器人的康复训练模式，为“人在回路”的同步评估-训练模式提供依据，为下肢远程康复训练和评估提供了一种潜在方法。
