SCH: Wireless battery-less smart sensing and analytic with application to wound assessment

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The goal of the proposed project is to develop smart and connected health sensors using soft and conformable materials for continuous monitoring via sweat and other biofluids. Integrated into bandages, dressings, diapers, or clothing, and powered wirelessly using WiFi, these would enable continuous measurement of multiple vitals, biomarkers, and metabolites for both healthy individuals and those with acute or chronic illness. An application that embodies the critical need for such sensors is the monitoring and care of wounds, especially chronic wounds and surgical site infections. Wound monitoring in the clinic is infrequent, and patients often must self-monitor and care for their wounds at home. To address this need, this proposal describes the design, implementation, and validation of a smart wound monitor that will assess wound status through multiple sensors, including temperature, moisture, pH, and targeted biomarkers. Acquiring these continuous signals in a useful manner requires sensors that can be integrated cheaply into a bandage or dressing, including wireless data readout and battery-less operation. Extracting prognosis prediction requires robust inference from time-varying and temperature-varying sensor data, in the presence of artifacts and environmental noise, requiring connected and intelligent analytics in real time. The proposed research includes the development of conformal electrochemical sensors for measurement of biological markers on soft fabrics; development of integrated circuits that extend the range of operation of RF-powered sensors; co-integration of sensors and electronics into a disposable, battery-less device using adaptive multi-sensor readout interface circuits for energy minimization; and, the application of advanced probabilistic classification methods to multi-sensor time-series data for novel prognosis prediction and health status modeling. The proposed approach would provide multiple benefits for the patient and care provider, including (i) automated feedback to the clinician on wound progression, (ii) feedback to the patient to reduce incidence of common wound-care mistakes, and (iii) early detection of infection, reducing infection-associated morbidity and mortality. The project additionally includes a validation plan using an artificial wound bed, followed by testing in animal model, as well as the development of materials for training patients, caregivers, and clinicians in the proper use of the smart wound monitor.