The Development of a Multimodal Sensing, 3D Closed-Loop Circuit for Accelerated Wound Healing

Abstract

Chronic and acute wounds, including diabetic foot ulcers, pressure ulcers, and traumatic injuries, impose substantial healthcare costs and diminish quality of life for millions of patients worldwide. Conventional wound dressings remain largely passive, offering protection and moisture retention but lacking the ability to actively promote healing. Electrical stimulation (ES) therapy has been shown to accelerate wound healing by restoring tissue-specific injury currents, promoting galvanotaxis of key cell types, and upregulating growth factors such as VEGF, FGF, and TGF-β. However, ES remains underutilized clinically due to bulky devices, inconsistent electrode placement and stimulation parameters, and the absence of real-time wound monitoring to guide treatment. This project addresses these barriers through the development of a textile-based, closed-loop smart dressing that integrates programmable ES electrodes and multimodal sensors into a flexible, embroidered fiber platform. Using an embroidery machine, we developed a protocol for constructing flexible biocircuits from silver fibers on multiple wound-relevant substrates, including gauze, veterinary sutures, medical bandages, and surgical mesh. This protocol enables the production of customized, reproducible circuits tailored to diverse wound geometries and anatomical sites at low cost. We validated the mechanical performance of the fabricated electrodes and confirmed their functionality in sensing temperature, humidity, and strain, as well as their capability to operate as ECG/EMG sensors. These preliminary results establish a standardized fabrication method that address the lack of reproducibility in electrode design and placement that has limited the clinical translation of ES therapy. This work provides a scalable foundation for future closed-loop, textile-integrated wound monitoring systems, with broader implications for advancing personalized, precision wound care. Future work will focus on optimizing battery performance, validating device function using ex vivo and in vivo models, and developing a machine-learning framework to interpret multimodal sensor data and support adaptive, safety-gated treatment recommendations.