| Abstract |
The purpose of my project was to determine whether it was possible to integrate sensors into a wound dressing, enabling them to monitor data and model whether a wound is infected. When triggered by the model, treatment would be administered by the system. This provides many benefits such as reducing limb fatality rates, reducing healing times, lowering healthcare costs and wait times, and improving quality of life. This project isn't only designed to be used in professional medical settings either; it can be used at home, particularly for elderly people who want to avoid unnecessary journeys to the hospital, and could put them at risk of further complications. It could also be used in sport settings, as it can act like a bandage, but also provide better conditions for healing and monitoring.
The project monitors key wound biomarkers such as pH, temperature and moisture, which are associated with infections. The project uses a Raspberry Pi to simulate data and send it to an app built on Flutter. This uses Google's Firebase for data logging. The data was processed against clinically-defined thresholds that could be changed remotely by a medical professional only.
Tests successfully demonstrated the system's ability to assess simulated infections.
The remote configuration and app, along with the conceptualised simulation of drug release, have also proven to work.
This project validated a viable and scalable software framework that now requires a prototype to test on, a step that promises to advance chronic wound care through data monitoring.
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