IEEE Int Conf Rehabil Robot. 2023 Sep;2023:1-6. doi: 10.1109/ICORR58425.2023.10304705.
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.
肌电图(EMG)是一种用于辅助和康复技术的手势控制的常用人机接口。即使由于虚弱或瘫痪而无法进行物理运动,肌电图也可以用于估计运动意图。肌电图传统上是从位于前臂的外在手部肌肉中记录的。然而,由于 EMG 传感器可以集成到腕戴式可穿戴设备(例如手表、手镯)中,因此腕部已成为商业应用中越来越有吸引力的记录位置。在这里,我们研究了从手腕而不是前臂记录肌电图对手部偏瘫中风患者的影响。我们发现,相对于瘫痪的前臂和非瘫痪的手腕,瘫痪的手腕上的肌电图信号噪声比明显更差。尽管如此,我们还发现,相对于瘫痪的前臂,从肌电图中对手势进行分类的能力要好得多。我们的研究结果还为每个记录位置提供了理想手势的指导。即,在瘫痪的一侧,从前臂和手腕 EMG 上识别单个数字手势似乎最为容易。这些结果表明,腕戴式 EMG 的商业化将通过提供更准确的 EMG 控制和更广泛采用的可穿戴外形,使中风患者受益。