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IEEE trans:用于脊髓损伤后上肢机器人康复的肌电控制接口

2021-06-22 MedSci原创 MedSci原创

中风引起的痉挛性轻瘫被认为会改变受损骨骼肌的结构。肌肉结构的改变可能意味着被动机械性能的改变,从而导致力量传递和本体感觉反馈的改变。这种变化反过来会影响甚至加重运动障碍。临床上,更好地了解被动肌肉特性

脊髓损伤(SCI)是脊柱损伤最严重的并发症,往往导致损伤节段以下肢体严重的功能障碍。脊髓损伤不仅会给患者本人带来身体和心理的严重伤害,还会对整个社会造成巨大的经济负担。由于脊髓损伤所导致的社会经济损失,针对脊髓损伤的预防、治疗和康复已成为当今医学界的一大课题。机器人康复已被证明是促进运动康复的有效疗法。表面肌电图(EMG)是通过皮肤表面局部电位变化间接测量肌肉收缩力,提供了一个促进患者参与康复的机会,为机器人康复干预提供了新的方案。

肌电图触发的神经肌肉刺激已被证明是中风恢复的有效治疗方法。根据感觉运动整合理论,当患者产生高于某一阈值的肌肉活动时,如肌电图所检测到的,则对肌肉施加辅助性电刺激。在这项工作中,提出了一个机器人康复系统,机器人协助取代电刺激。通过同步有意识产生的肌电图与本体感觉反馈来增强运动学习。

Fig. 1. - The MAHI Exo-II rehabilitation exoskeleton, capable of providing independent joint torques for four anatomical degrees of freedom. This user’s arm is fitted with surface EMG electrodes at eight locations covering muscles that control the elbow and wrist.

骨骼机器人

肌电控制界面设计为六种控制模式:四个单自由度(对应于外骨骼的主动自由度)和两个多自由度。单自由度模式为肘关节屈曲/伸展(E-Flx/Ext)、前臂旋前/旋后(F-Pro/Sup)、腕关节屈曲/伸展(W-Flx/Ext)和腕关节桡/尺偏(W-Rad/Uln)。多自由度模式为肘屈/伸联合前臂旋前/旋后(EF-multi)和腕屈/伸联合腕桡/尺偏(WW-multi)。对于每种控制模式,训练LDA分类器,利用在短暂等长收缩期间收集的肌电信号的特征来预测用户预期运动的方向。对于每个单自由度模式,有两个可能的分类方向,对于每个多自由度模式,有四个可能的分类方向。

从控制肘部、前臂和手腕运动的八块肌肉记录肌电信号:肱二头肌(BB)、肱三头肌(TB)、旋前圆肌(PT)、旋后肌(S)、桡侧腕屈肌(FCR)、尺侧腕伸肌(ECU)、桡侧腕长伸肌(ECRL)和尺侧腕屈肌(FCU)。试验的主要目的是为每个参与者和每个控制模式生成一个独特的分类器,以比较单自由度和多自由度模式之间以及健全和SCI参与者之间的性能。对于每种情况(即参与者和控制模式),方案分为三个阶段:主动收缩与静止状态的校准;训练数据采集和分类器拟合;分类器性能的在线测试。10名健康个体和4名SCI患者参与试验。

MAHI Exo II外骨骼配有可调配重,用于肘关节的被动重力补偿。在实验的各个阶段,采用比例微分反馈对机器人进行位置控制。当提供机器人运动反馈时,机器人遵循预先编程的参考轨迹。调整mahiexo-II的高度和肩外展角度,使参与者能够在坐姿时将手臂保持在自然姿势,肘部弯曲。调整椅子相对于外骨骼的位置,以保持两个肩膀的高度相等,并保持肩部在肩胛平面(~30°)。参与者被要求在测试过程中不要移动躯干或肩膀,但没有使用约束装置来强制执行。手腕手柄的位置是为了在参与者自然握持时提供最大的运动范围。设置肘关节配重,使参与者能够在机器人肘关节处于90度时休息弯曲的感觉。为了训练方向分类器,用户在每个目标的方向上进行等距收缩,以使分类器适合用户特定的肌电活动。视觉目标以随机顺序呈现,当检测到用户从静止状态到活动状态的转换时,从最后200ms的肌电数据中提取完整的特征集。

Fig. 3. - Approximate locations for each EMG electrode labeled by muscle. Muscles that contribute primarily to E-Flx/Ext are marked in blue, F-Pro/Sup in red, W-Flx/Ext in green, and W-Rad/Uln in purple. The right portion of the figure shows examples of filtered EMG waveforms from an isometric contraction in the Elbow-Forearm multi-DoF mode.

肌电图

基于肌电图的分类器能够在不同的模式下对使用者的预期运动方向进行高精度的分类。健康组的分类结果比SCI组更准确。对于身体健全的参与者,单自由度模式的分类准确率在大多数情况下达到100.0%,只有4例下降到95.0%。对于多自由度模式,除两名参与者外,所有参与者的分类准确率均在85.0%或以上。对于SCI 1和SCI 4两个受试者,单自由度分类准确率与健全组一样高。SCI2的单自由度表现与健康组基本相当,而SCI3的单自由度表现与健康组相比较差。每种模式的分类测试通常在两种情况下进行:一是无机器人运动反馈,二是有机器人运动反馈,即机器人运动到分类器预测的目标。

研究结果鼓励了康复机器人肌电控制接口的进一步发展。单自由度控制模式的平均分类精度在85.0%~95.0%之间,多自由度控制模式的平均分类精度在60%以上。考虑到颈椎损伤对运动功能影响的严重性,SCI组分类器的性能与健康组不匹配是正常的。利用肌电图实时控制外骨骼显示了系统的应用前景,特别是在简单的单自由度方向分类任务中。 

C. G. McDonald, J. L. Sullivan, T. A. Dennis and M. K. O’Malley, "A Myoelectric Control Interface for Upper-Limb Robotic Rehabilitation Following Spinal Cord Injury," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 4, pp. 978-987, April 2020, doi: 10.1109/TNSRE.2020.2979743.

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