SMART FOOT MONITORING: A CUTTING-EDGE SOLUTION FOR EARLY DETECTION OF DIABETIC COMPLICATIONS USING CNN MODEL

Abstract

The proposed system for early detection of diabetic complications incorporates advanced technology into conventional algorithms, employing intelligent sensors that communicate to a Raspberry Pi with the Internet of Things infrastructure. It significantly improves the therapy that is provided to diabetic patients. The processing of real-time data, which may include heel pressure, temperatures, and habits of motion, is carried out using a Convolutional Neural Network (CNN) model. The CNN can differentiate between healthy foot health and the early indicators of chronic problems, which enables distant surveillance and prompt treatments to reduce the risk of the consequences of diabetes. A cloud-based infrastructure provides physicians with quick notifications, whereas a user-friendly experience allows individuals to take control of their health. The implementation of this innovative solution represents a big step forward in the treatment of diabetic foot conditions. It offers a proactive approach to ensuring the health and well-being of patients and improving systemic administration. The effortless adoption of the system into everyday life highlights its potential to transform diabetic treatment practices. This makes it possible to ensure a comprehensive, successful technique for minimizing foot issues in those who have diabetics.

Keywords

• convolutional neural networks, raspberry pi, internet of things, diabetic patients, heel pressure.