手腕肌电图改善中风患者的手势分类。

Connor D Olsen, W Caden Hamrick, Samuel R Lewis, Marta M Iverson, Jacob A George
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引用次数: 0

摘要

肌电图(EMG)是一种流行的用于辅助和康复技术手势控制的人机界面。EMG可以用来估计运动意图,即使当一个人由于虚弱或瘫痪而无法移动时也是如此。EMG传统上是从前臂的外部手部肌肉记录的。然而,对于商业应用来说,手腕已经成为一个越来越有吸引力的记录位置,因为EMG传感器可以集成到手腕佩戴的可穿戴设备(例如手表、手镯)中。在这里,我们探讨了从手腕而不是前臂记录肌电图对上肢偏瘫中风患者的影响。我们发现,相对于偏瘫前臂和非偏瘫手腕,偏瘫手腕的肌电图信噪比明显较差。尽管如此,我们还表明,相对于偏瘫前臂,从肌电图中对手势进行分类的能力在偏瘫手腕处明显更好。我们的研究结果还为每个录音位置的理想手势提供了指导。也就是说,从偏瘫侧的前臂和手腕肌电图来看,个位数手势似乎最容易分类。这些结果表明,手腕佩戴的EMG的商业化将有利于中风患者,因为它以更广泛采用的可穿戴形式提供了更准确的EMG控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wrist EMG Improves Gesture Classification for Stroke Patients.

Electromyography (EMG) is a popular human-machine interface for hand gesture control of assistive and rehabilitative technology. EMG can be used to estimate motor intent even when an individual cannot physically move due to weakness or paralysis. EMG is traditionally recorded from the extrinsic hand muscles located in the forearm. However, the wrist has become an increasingly attractive recording location for commercial applications as EMG sensors can be integrated into wrist-worn wearables (e.g., watches, bracelets). Here we explored the impact that recording EMG from the wrist, instead of the forearm, has on stroke patients with upper-limb hemiparesis. We show that EMG signal-to-noise ratio is significantly worse at the paretic wrist relative to the paretic forearm and non-paretic wrist. Despite this, we also show that the ability to classify hand gestures from EMG was significantly better at the paretic wrist relative to the paretic forearm. Our results also provide guidance as to the ideal gestures for each recording location. Namely, single-digit gestures appeared easiest to classify from both forearm and wrist EMG on the paretic side. These results suggest commercialization of wrist-worn EMG would benefit stroke patients by providing more accurate EMG control in a more widely adopted wearable formfactor.

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