Lan Yiyun, Yao Jun, Dewald Julius P A
Interdepartmental Neuroscience Program and Department of Physical Therapy and Human Movement Sciences, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4136-9. doi: 10.1109/IEMBS.2011.6091027.
Many stroke patients are subject to limited hand functions in the paretic arm due to a significant loss of Corticospinal Tract (CST) fibers. A possible solution for this problem is to classify surface Electromyography (EMG) signals generated by hand movements and uses that to implement Functional Electrical Stimulation (FES). However, EMG usually presents an abnormal muscle coactivation pattern shown as increased coupling between muscles within and/or across joints after stroke. The resulting Abnormal Muscle Synergies (AMS) could make the classification more difficult in individuals with stroke, especially when attempting to use the hand together with other joints in the paretic arm. Therefore, this study is aimed at identifying the impact of AMS following stroke on EMG pattern recognition between two hand movements. In an effort to achieve this goal, 7 chronic hemiparetic chronic stroke subjects were recruited and asked to perform hand opening and closing movements at their paretic arm while being either fully supported by a virtual table or loaded with 25% of subject's maximum shoulder abduction force. During the execution of motor tasks EMG signals from the wrist flexors and extensors were simultaneously acquired. Our results showed that increased synergy-induced activity at elbow flexors, induced by increasing shoulder abduction loading, deteriorated the performance of EMG pattern recognition for hand opening for those with a weak grasp strength and EMG activity. However, no such impact on hand closing has yet been observed possibly because finger/wrist flexion is facilitated by the shoulder abduction-induced flexion synergy.
许多中风患者因皮质脊髓束(CST)纤维大量丧失,患侧手臂手部功能受限。解决这一问题的一种可能方法是对手部运动产生的表面肌电图(EMG)信号进行分类,并利用其实施功能性电刺激(FES)。然而,中风后肌电图通常呈现出异常的肌肉共同激活模式,表现为关节内和/或关节间肌肉之间的耦合增加。由此产生的异常肌肉协同作用(AMS)可能会使中风患者的分类更加困难,尤其是当试图将手部与患侧手臂的其他关节一起使用时。因此,本研究旨在确定中风后AMS对两种手部运动之间肌电图模式识别的影响。为了实现这一目标,招募了7名慢性偏瘫慢性中风患者,要求他们在患侧手臂进行手部开合动作,同时要么由虚拟桌子完全支撑,要么承受受试者最大肩部外展力的25%。在执行运动任务期间,同时采集来自腕部屈肌和伸肌的肌电图信号。我们的结果表明,增加肩部外展负荷诱导的肘部屈肌协同诱导活动增加,会降低抓握力和肌电图活动较弱的患者手部张开肌电图模式识别的性能。然而,尚未观察到对手部闭合有此类影响,可能是因为肩部外展诱导的屈曲协同作用促进了手指/腕部屈曲。