Department of Rehabilitation & Prevention Engineering, Institute of Applied Medical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.
Sensors (Basel). 2020 Dec 21;20(24):7348. doi: 10.3390/s20247348.
Wearable devices play an increasing role in the rehabilitation of patients with movement disorders. Although information about muscular activation is highly interesting, no approach exists that allows reliable collection of this information when the sensor is applied autonomously by the patient. This paper aims to demonstrate the proof-of-principle of an innovative sEMG sensor system, which can be used intuitively by patients while detecting their muscular activation with sufficient accuracy. The sEMG sensor system utilizes a multichannel approach based on 16 sEMG leads arranged circularly around the limb. Its design enables a stable contact between the skin surface and the system's dry electrodes, fulfills the SENIAM recommendations regarding the electrode size and inter-electrode distance and facilitates a high temporal resolution. The proof-of-principle was demonstrated by elbow flexion/extension movements of 10 subjects, proving that it has root mean square values and a signal-to-noise ratio comparable to commercial systems based on pre-gelled electrodes. Furthermore, it can be easily placed and removed by patients with reduced arm function and without detailed knowledge about the exact positioning of the sEMG electrodes. With its features, the demonstration of the sEMG sensor system's proof-of-principle positions it as a wearable device that has the potential to monitor muscular activation in home and community settings.
可穿戴设备在运动障碍患者的康复中发挥着越来越重要的作用。虽然肌肉活动信息非常有趣,但目前还没有一种方法可以在传感器由患者自主应用时可靠地收集这些信息。本文旨在展示一种创新的表面肌电传感器系统的原理验证,该系统可以让患者直观地使用,同时具有足够的准确性来检测他们的肌肉活动。该表面肌电传感器系统采用基于 16 个表面肌电导联的多通道方法,这些导联围绕肢体呈圆形排列。其设计实现了皮肤表面与系统干电极之间的稳定接触,符合 SENIAM 关于电极尺寸和电极间距离的建议,并实现了高时间分辨率。通过 10 名受试者的肘部屈伸运动证明了其原理验证,证明其均方根值和信噪比可与基于预凝胶电极的商业系统相媲美。此外,它可以由手臂功能降低的患者轻松放置和移除,而无需详细了解表面肌电电极的精确位置。该表面肌电传感器系统的原理验证展示了其具有作为可穿戴设备的潜力,可以在家中和社区环境中监测肌肉活动。