Early detection of diabetic foot ulcer using IoT and ML

Abstract

This study explores the critical realm of Diabetic Foot Ulcers (DFUs) and proposes an innovative approach for early detection using Internet of Things (IoT) and Machine Learning (ML). A chronic metabolic condition with elevated blood glucose levels is called diabetes mellitus. A foot ulcer is an open wound that is typically located beneath the feet. It can be shallow and less severe, occurring just below the skin's surface, or it can be deep and expose the bones, tendons, and joints. However, diabetes patients may be able to avoid complications from diabetic foot ulcers if early prophylaxis is practiced. One of the complications that this condition is frequently linked to is diabetic foot ulcers. Focusing on Diabetes Mellitus, the chronic metabolic condition leading to DFUs, the study introduces a wearable shoe prototype equipped with temperature and pressure sensors. This IoT-enabled device facilitates daily foot evaluation at home, allowing for timely identification of early symptoms and severity monitoring. By integrating ML algorithms, the real-time ulcer detection system aims to prevent complications, reduce amputations, and enhance proactive diabetic care.

Keywords

• diabetic foot ulcer
• diabetes mellitus
• sensors
• wearable shoe
• internet of things
• ml algorithms
• alert system