State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
J Neural Eng. 2024 Aug 8;21(4). doi: 10.1088/1741-2552/ad68a5.
In recent years, the robot assisted (RA) rehabilitation training has been widely used to counteract defects of the manual one provided by physiotherapists. However, since the proprioception feedback provided by the robotic assistance or the manual methods is relatively weak for the paralyzed patients, their rehabilitation efficiency is still limited. In this study, a dynamic electrical stimulation (DES) based proprioception enhancement and the associated quantitative analysis methods have been proposed to overcome the limitation mentioned above.Firstly, the DES based proprioception enhancement method was proposed for the RA neural rehabilitation. In the method, the relationship between the surface electromyogram (sEMG) envelope of the specified muscle and the associated joint angles was constructed, and the electrical stimulation (ES) pulses for the certain joint angles were designed by consideration of the corresponding sEMG envelope, based on which the ES can be dynamically regulated during the rehabilitation training. Secondly, power spectral density, source estimation, and event-related desynchronization of electroencephalogram, were combinedly used to quantitatively analyze the proprioception from multiple perspectives, based on which more comprehensive and reliable analysis results can be obtained. Thirdly, four modes of rehabilitation training tasks, namely active, RA, DES-RA, and ES-only training, were designed for the comparison experiment and validation of the proposed DES based proprioception enhancement method.The results indicated that the activation of the sensorimotor cortex was significantly enhanced when the DES was added, and the cortex activation for the DES-RA training was similar to that for the active training. Meanwhile, relatively consistent results from the multiple perspectives were obtained, which validates the effectiveness and robustness of the proposed proprioception analysis method.The proposed methods have the potential to be applied in the practical rehabilitation training to improve the rehabilitation efficiency.
近年来,机器人辅助(RA)康复训练已广泛用于弥补物理治疗师提供的手动康复训练的缺陷。然而,由于机器人辅助或手动方法提供的本体感觉反馈对于瘫痪患者相对较弱,因此其康复效率仍然有限。在这项研究中,提出了一种基于动态电刺激(DES)的本体感觉增强及其相关的定量分析方法,以克服上述限制。
首先,提出了一种基于 DES 的 RA 神经康复的本体感觉增强方法。在该方法中,构建了指定肌肉的表面肌电图(sEMG)包络与相关关节角度之间的关系,并考虑相应的 sEMG 包络设计了针对特定关节角度的电刺激(ES)脉冲,基于此,可以在康复训练过程中动态调节 ES。
其次,联合使用功率谱密度、源估计和事件相关去同步化脑电图,从多个角度对本体感觉进行定量分析,从而可以获得更全面、更可靠的分析结果。
最后,设计了四种康复训练任务模式,即主动、RA、DES-RA 和仅 ES 训练,用于对比实验和验证所提出的基于 DES 的本体感觉增强方法。
结果表明,添加 DES 后,感觉运动皮层的激活显著增强,DES-RA 训练的皮层激活与主动训练相似。同时,从多个角度获得了相对一致的结果,验证了所提出的本体感觉分析方法的有效性和鲁棒性。
所提出的方法有可能应用于实际的康复训练中,以提高康复效率。