Assessment of liquid intake is necessary to obtain a complete picture of an individual's hydration status. Measurements using state-of-theart 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. Here, an 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 component of the device is a composite piezoresistive sensor consisting of single-layer graphene decorated with metallic nanoislands and coated with a highly p l a s t i c i z e d f o r m o f t h e c o n d u c t i v e p o l y m e r p o l y ( 3 , 4ethylenedioxythiophene):poly(styrenesuflonate) (PEDOT:PSS). Surface electromyography (sEMG) signals obtained with conventional electrodes are used in concert with the strain measurements. The use of strain and sEMG measurements together both (1) improve the accuracy of estimated volumes and (2) permit the differentiation of swallowing from motion artifacts. In a cohort consisting of 11 participants, the combined measurements of strain and sEMGprocessed by the machine learning algorithmwere able to estimate unknown swallowed volumes cumulatively between 5 and 30 mL of water with greater than 92% accuracy. Ultimately, this system holds promise for numerous applications in sports medicine, rehabilitation, and the detection of nascent dysfunction in swallowing.

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.

Implantable devices that detect and treat diseases without any intervention required from the patient are expected to be the trend of the future. This paper presents a peizo electrically controlled MEMS drug delivery device for on-demand release of defined quantities of drug in a sustained and controlled manner. A drug-loaded polymer based micro reservoir (600µm ×550µm) is sealed by a Polydimethylsiloxane (PDMS) membrane placed over the drug reservoir on which the piezoelectric material is deposited. On application of voltage across this piezoelectric material, the membrane deflects allowing the fluid to fill into the chamber that will mix with the drug and due to concentration variation; the drug would come off the reservoir or vice versa. A 0.3µm-thick PZT material is deposited on 20µm PDMS membrane. Discharge of the drug solution and the release rates were controlled by an external electric field. Characterization of the devices was implemented in-vitro using the colored water solution. The reservoir was capable of delivering 20µl drug on application of 10V.