Decoding movement intent patterns based on spatiotemporal and adaptive filtering method towards active motor training in stroke rehabilitation systems
Decoding movement intent patterns based on spatiotemporal and adaptive filtering method towards active motor training in stroke rehabilitation systems
复制标题
基于时空和自适应过滤方法的运动意图模式解码，用于中风康复系统中的主动运动训练
DOI：
10.1007/s00521-020-05536-9
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发表时间：
2021-01-03
影响因子：
6
通讯作者：
Li, Guanglin
中科院分区：
文献类型：
--
作者：
Samuel, Oluwarotimi Williams;Asogbon, Mojisola Grace;Li, Guanglin
上肢（UE）神经肌肉功能障碍严重影响中风后患者进行日常生活活动。在这方面，已经开发了各种康复机器人，用于提供辅助力和/或阻力，其允许中风幸存者训练他们的手臂以恢复失去的手臂功能。然而，大多数康复系统被动地起作用，使得它们仅允许患者导航通常与其UE移动意图不一致的已经定义的轨迹，从而阻碍充分的运动功能恢复。解决这个问题的一种可能的方法是使用解码的UE运动意图来触发患者的主动和直观的运动训练，这将有助于恢复他们的UE手臂功能。在这项研究中，提出了一种基于时空神经肌肉描述符和自适应滤波技术（STD-AFT）的新方法，以最佳地表征中风后患者的UE运动的多种模式，为康复机器人系统中的智能驱动运动训练提供输入。通过从进行21种不同类别的预定义肢体运动的卒中后幸存者获得的高密度表面肌电图记录，与常用方法相比，系统地研究和评估了拟议的STD-AFT性能。此外，使用四种不同的分类算法进行运动意图解码。实验结果表明，提出的STD-AFT实现了显着的改善高达13.36%（p< 0.05），在表征运动意图的多种模式，相对较低的标准误差值，即使在存在的外部干扰的形式的噪声相比，现有的基准方法。此外，STD-AFT在二维空间中表现出明显的模式分离性。这项研究的结果表明，拟议的STD-AFT可以提供潜在的输入，主动和直观的运动训练的机器人系统，针对中风康复。
Upper extremity (UE) neuromuscular dysfunction critically affects post-stroke patients from performing activities of daily life. In this regard, various rehabilitation robotics have been developed for providing assistive and/or resistive forces that allow stroke survivors to train their arms towards regaining the lost arm function. However, most of the rehabilitation systems function in a passively such that they only allow patients navigate already-defined trajectories that often does not align with their UE movement intention, thus hindering adequate motor function recovery. One possible way to address this problem is to use a decoded UE motion intent to trigger active and intuitive motor training for the patients, which would help restore their UE arm functions. In this study, a new approach based on spatiotemporal neuromuscular descriptor and adaptive filtering technique (STD-AFT) is proposed to optimally characterize multiple patterns of UE movements in post-stroke patients towards providing inputs for intelligently driven motor training in the rehabilitation robotic systems. The proposed STD-AFT performance was systematically investigated and assessed in comparison with commonly adopted methods via high-density surface electromyogram recordings obtained from post-stroke survivors who performed 21 distinct classes of pre-defined limb movements. Furthermore, the movement intent decoding was done using four different classification algorithms. The experimental results showed that the proposed STD-AFT achieved significant improvement of up to 13.36% (p< 0.05) in characterizing the multiple patterns of movement intents with relatively lower standard-error value even in the presence of the external interference in form of noise compared to the existing benchmark methods. Also, the STD-AFT showed obvious pattern seperability for individual movement class in a two-dimensional space. The outcomes of this study suggest that the proposed STD-AFT could provide potential inputs for active and intuitive motor training in robotic systems targeted towards stroke-rehabilitation.