Wearable sensors show much promise for medical, sports, defense, emergency, and consumer applications, but are currently limited to obtrusive implementations. Akin to the evolution of cell phones that evolved from foot-long prototypes to recent smart devices, next-generation wearables are envisioned to be seamlessly embedded in fabrics. This CAREER project aims to understand the unique challenges of operating such textile sensors ?in-the-wild? and to empower their reliable operation via closed-loop interaction among fabrics, electronics, and humans. To serve as a model and to inspire new applications, the project focuses on new classes of functionalized garments that can seamlessly monitor kinematics and/or tissue abnormalities with unique advantages over the state-of-the-art. Concurrently, the integrated education/outreach efforts aim to increase student and public exposure to bio-electromagnetics that are now confined to specialized research, yet can enable interdisciplinary training for all via appealing activities with direct societal impact.

This CAREER project will pioneer a design, modeling, and implementation framework that reconciles human-in-the-loop Cyber-Physical Systems (CPS) with conductive e-textile sensors operating in complex (human wearing a sensing fabric) and dynamic (real-world) environments. Cognitive and fully-adaptive e-textile CPS are proposed that: (a) are cognizant of inputs received by the wearer, the fabric, and the environment, and (b) integrate agility in both the cyber and physical sides for closed-loop adaptability on the fly. In turn, potentials in optimizing performance, minimizing resources, and enhancing opportunities for myriads of human-in-the-loop CPS are envisioned to be significant. Without loss of generality, focus is on a novel-multi-utility sensor that addresses two of the most challenging sensing modalities in the area of wearables, i.e., motion capture and tissue abnormality monitoring. These modalities may be individually or concurrently employed to create models for dense-data (motion captured on the go), sparse-data (tissues monitored over sparse intervals), and context-aware (both of the above) human-in-the-loop CPS. As a case study, a novel CPS will be progressively designed ? from concept to in vivo testing ? to improve outcomes after anterior cruciate ligament reconstruction.