Surface electromyography (sEMG) is widely used in monitoring human health. Nonetheless, it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces such as the larynx, because regular sEMG electrodes have stiff structures. In this study, we developed a stretchable, high-density sEMG electrode array via layer-by-layer printing and lamination. The electrode offered a series of excellent human‒machine interface features, including conformal adhesion to the skin, high electron-to-ion conductivity (and thus lower contact impedance), prolonged environmental adaptability to resist water evaporation, and epidermal biocompatibility. This made the electrode more appropriate than commercial electrodes for long-term wearable, high-fidelity sEMG recording devices at complicated skin interfaces. Systematic in vivo studies were used to investigate its ability to classify swallowing activities, which was accomplished with high accuracy by decoding the sEMG signals from the chin via integration with an ear-mounted wearable system and machine learning algorithms. The results demonstrated the clinical feasibility of the system for noninvasive and comfortable recognition of swallowing motions for comfortable dysphagia rehabilitation.

表面肌电图（sEMG）广泛用于监测人类健康。尽管如此，在具有复杂曲面的区域（例如喉部）捕获高保真 sEMG 记录具有挑战性，因为常规 sEMG 电极具有刚性结构。在这项研究中，我们通过逐层印刷和层压开发了一种可拉伸、高密度的表面肌电电极阵列。该电极具有一系列优异的人机界面特性，包括对皮肤的保形粘附、高电子-离子电导率（从而降低接触阻抗）、长期抵抗水分蒸发的环境适应性以及表皮生物相容性。这使得该电极比商业电极更适合用于复杂皮肤界面的长期可穿戴、高保真表面肌电图记录设备。系统性的体内研究被用来调查其对吞咽活动进行分类的能力，这是通过与耳挂式可穿戴系统和机器学习算法集成解码来自下巴的 sEMG 信号来高精度完成的。结果证明了该系统用于无创、舒适地识别吞咽运动以实现舒适的吞咽困难康复的临床可行性。