Volume - 7 | Issue - 2 | june 2025
Published
22 July, 2025
There are serious problems with chronic wounds like bedsores and diabetic ulcers, which are observed and treated with the current wound care methods that mainly use traditional bandages. These bandages only provide basic security and are unable to track wound data like infection and SpO2 levels. This may lead to delayed detection of problems and prolonged healing times. A smart bandage with several sensors is proposed in this method to overcome the limitations. The developed system’s temperature, pH and SpO2 sensors are mostly used to monitor the wounds. Data from these different sensors is processed by an embedded TinyML-based AI system to assess and provide wound healing stages, detect infections, and notify users regarding infection levels. Important measures, including temperature, pH, and SpO2 levels, are displayed in a developed application so that patients and doctors can track progress and receive real-time notifications. Using machine learning algorithms, the data will analyze the wound healing phases and also have the ability to perform real-time monitoring and infection detection quickly. This smart sensor represents a significant advance over traditional approaches for better wound care management.
KeywordsSmart Bandage Sensors Chronic Wound Wound Monitoring Artificial Intelligence Machine Learning
