External Measurement of Swallowed Volume During Exercise Enabled by Stretchable Derivatives of PEDOT:PSS, Graphene, Metallic Nanoparticles, and Machine Learning
External Measurement of Swallowed Volume During Exercise Enabled by Stretchable Derivatives of PEDOT:PSS, Graphene, Metallic Nanoparticles, and Machine Learning
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DOI：
10.1002/adsr.202200060
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发表时间：
2023-01
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通讯作者：
Beril Polat;Tarek Rafeedi;Laura Becerra;Alexander X. Chen;Kuanjung Chiang;V. Kaipu;Rachel Blau;P. Mercier;Chung-Kuan Cheng;D. Lipomi
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作者：
Beril Polat;Tarek Rafeedi;Laura Becerra;Alexander X. Chen;Kuanjung Chiang;V. Kaipu;Rachel Blau;P. Mercier;Chung-Kuan Cheng;D. Lipomi
用于远程医疗保健和性能监测的表皮传感器需要能够在身体运动、热和出汗的影响下操作。在此，评估了使用专门合成的聚合物基干电极和石墨烯基应变计来测量典型运动条件下的吞咽量。电极由静电结合到聚（苯乙烯磺酸盐）-B-聚（聚（乙二醇）甲基醚丙烯酸酯）（PSS-B-PPEGMEA）上的常见导电聚合物聚（3，4亚乙基二氧噻吩）（PEDOT）组成，收集颏下肌群上的表面肌电图（sEMG）信号。同时，使用应变计测量皮肤表面的变形，应变计包含单层石墨烯，支持金的亚连续覆盖和含有PEDOT：PSS的高度增塑复合材料。这些材料一起允许高拉伸性、高分辨率和耐汗性。定制的印刷电路板（PCB）允许该多组件系统无线采集应变和表面肌电信号数据。该传感器平台在10名受试者的队列中进行吞咽活动测试，同时在固定自行车上步行或骑自行车。使用机器学习（ML）模型，可以预测吞咽量，步行的绝对误差为36%，骑自行车的绝对误差为43%。
Epidermal sensors for remote healthcare and performance monitoring require the ability to operate under the effects of bodily motion, heat, and perspiration. Here, the use of purpose‐synthesized polymer‐based dry electrodes and graphene‐based strain gauges to obtain measurements of swallowed volume under typical conditions of exercise is evaluated. The electrodes, composed of the common conductive polymer poly(3,4 ethylenedioxythiophene) (PEDOT) electrostatically bound to poly(styrenesulfonate)‐b‐poly(poly(ethylene glycol) methyl ether acrylate) (PSS‐b‐PPEGMEA), collect surface electromyography (sEMG) signals on the submental muscle group, under the chin. Simultaneously, the deformation of the surface of the skin is measured using strain gauges comprising single‐layer graphene supporting subcontinuous coverage of gold and a highly plasticized composite containing PEDOT:PSS. Together, these materials permit high stretchability, high resolution, and resistance to sweat. A custom printed circuit board (PCB) allows this multicomponent system to acquire strain and sEMG data wirelessly. This sensor platform is tested on the swallowing activity of a cohort of 10 subjects while walking or cycling on a stationary bike. Using a machine learning (ML) model, it is possible to predict swallowed volume with absolute errors of 36% for walking and 43% for cycling.