UC San Diego

Assessment of liquid intake is necessary to obtain a complete picture of an individual’s hydration status. Measurements using state-of-the-art wearable devices have been demonstrated, but none of these devices have combined high sensitivity, unobtrusiveness, and automated estimation of volume, i.e., using machine learning. Such a capability would have immense value in a variety of medical contexts, such as monitoring patients with dysphagia or the performance of athletes. This dissertation first surveys the literature for existing materials-enabled sensors used to detect and/or monitor swallowing activity. Next, a new epidermal sensor platform is introduced to address the issues that still exist in this field. This epidermal sensor platform is combined with machine learning to measure swallowed liquid volume based on signals obtained from the surface of the skin. The key sensors of the device are a composite piezoresistive sensor consisting of single-layer graphene decorated with gold nanoislands and coated with a highly plasticized form of the conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesuflonate) (PEDOT:PSS) and a surface electromyography (sEMG) sensor comprised of PEDOT that is electrostatically bound to poly(styrenesulfonate)-b-poly(poly(ethylene glycol) methyl ether acrylate) (PSS-b-PPEGMEA). The use of strain and sEMG measurements together both (1) improve the accuracy of estimated swallowed volumes and (2) permit the differentiation of swallowing from motion artifacts. Finally, the performance of this sensor platform is tested with two cohort studies where participants are in a resting position or exercising during a swallow. The combined measurements of strain and sEMG from these studies—processed by the machine learning algorithm—can estimate unknown swallowed volumes cumulatively between 5 to 30 mL of water. Ultimately, this system holds promise for numerous applications in sports medicine, rehabilitation, and the detection of nascent dysfunction in swallowing.