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自生肌电控制功能性电刺激用于踝关节背屈控制。

Autogenic EMG-controlled functional electrical stimulation for ankle dorsiflexion control.

机构信息

Department of Medical Engineering, Eulji University, Sungnam, Gyeonggi, South Korea.

出版信息

J Neurosci Methods. 2010 Oct 30;193(1):118-25. doi: 10.1016/j.jneumeth.2010.08.011. Epub 2010 Aug 14.

Abstract

Our objectives were to develop and test a new system for the potential for stable, real-time cancellation of residual stimulation artefacts (RSA) using surface electrode autogenic electromyography-controlled functional electrical stimulator (aEMGcFES). This type of closed-loop FES could be used to provide more natural, continuous control of lower extremity paretic muscles. We built upon work that has been done in the field of FES with one major technological innovation, an adaptive Gram-Schmidt filtering algorithm, which allowed us to digitally cancel RSA in real-time. This filtering algorithm resulted in a stable real-time estimation of the volitional intent of the stimulated muscle, which then acted as the direct signal for continuously controlling homonymous muscle stimulation. As a first step toward clinical application, we tested the viability of our aEMGcFES system to continuously control ankle dorsiflexion in a healthy subject. Our results indicate positively that an aEMGcFES device with adaptive filtering can respond proportionally to voluntary EMG and activate forceful movements to assist dorsiflexion during controlled isometric activation at the ankle. We also verified that normal ankle joint range of movement could be maintained while using the aEMGcFES system. We suggest that real-time cancellation of both primary and RSA is possible with surface electrode aEMGcFES in healthy subjects and shows promising potential for future clinical application to gait pathologies such as drop foot related to hemiparetic stroke.

摘要

我们的目标是开发和测试一种新系统,以利用表面电极自生肌电控制功能性电刺激(aEMGcFES)来稳定、实时地消除残留刺激伪迹(RSA)。这种闭环 FES 可用于提供更自然、连续的下肢瘫痪肌肉控制。我们在 FES 领域的工作基础上进行了一项重大技术创新,即自适应 Gram-Schmidt 滤波算法,该算法使我们能够实时数字消除 RSA。这种滤波算法可以稳定实时地估计刺激肌肉的随意意图,然后将其作为连续控制同源肌肉刺激的直接信号。作为临床应用的第一步,我们测试了我们的 aEMGcFES 系统在健康受试者中连续控制踝关节背屈的可行性。我们的结果积极表明,具有自适应滤波的 aEMGcFES 设备可以对自愿性肌电图做出比例响应,并在踝关节等长激活时激活有力的运动来辅助背屈。我们还验证了在使用 aEMGcFES 系统时可以保持正常的踝关节活动范围。我们认为,在健康受试者中,表面电极 aEMGcFES 可以实时消除主要刺激伪迹和 RSA,这为未来应用于步态病理(如与偏瘫性中风相关的足下垂)提供了有希望的临床应用潜力。

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