TWINSENSE

TWINSENSE is a project aimed at digitizing wound management through the development of a digital twin (DT) of chronic wounds through a multiplexed sensor system. The project integrates data from a custom-designed, wearable multimodal sensor with machine learning (ML) algorithms to develop the DT, enabling real-time analysis of wound biomarkers and accurate prediction of healing outcomes. This will facilitate timely therapeutic interventions, reducing the risk of complications such as amputations in chronic wounds. TWINSENSE focuses on developing screen-printable formulations of metal-decorated reduced graphene oxide (rGO) derivatives to create multimodal sensors on wearable bandages. These sensors, in combination with a wearable electronic circuit, will continuously monitor wound parameters (pH, temperature, urea) to generate comprehensive ML training datasets. The datasets will be employed to develop the DT using knowledge graph-based data modelling techniques. The DT will also feature a Scenario-Maker module, capable of processing data from the multimodal sensors to simulate various healing scenarios, predicting potential complications, and recommending optimized treatment strategies in real time. By addressing the unmet need for advanced chronic wound care, which affects over 60 million people globally, TWINSENSE aims to improve patient outcomes and reduce healthcare costs. Additionally, TWINSENSE has the potential to foster an entrepreneurial ecosystem around digital wound care technologies, driving revenue generation and contributing to the sustainable development of healthcare systems.