IoT-Driven Early Warning System for Diabetic Foot Ulcer Deploying ML Algorithm

Patients with long-term diabetes and poor glycemic index often develop peripheral neurovascular disorder. This neurovascular complication often results in Diabetic Foot Ulcer (DFU). Approximately 40 to 50% of patients are affected by foot ulceration who are diagnosed with diabetics. It is analyzed that approximately 20% of hospitalization among patients concerned to diabetes mellitus is due to foot ulceration. Hence, early detection and prevention of diabetic foot ulcers are essential. This paper focuses on early detection of chronic foot lesions such as blisters, fasciitis, and callus in diabetic patients by monitoring foot pressure. This project aims to develop a cost-efficient, portable foot pressure smart insole and check its dependability and credibility to prevent foot ulceration. Hardware is set up to monitor foot pressure in diabetic patients continuously via a flexi-force sensor. This is accomplished by placing pressure sensors at five pressure points in a smart insole which is then used by the patient, and made user-friendly by transmitting and storing the results on a server. The proposed Internet of Things (IoT)-based smart insole makes it easier to evaluate the patient’s feet every day at home, enabling prompt detection of early signs and severity tracking. The goal of real-time ulcer detection system is to improve proactive diabetic treatment, decrease amputations, and avoid complications by incorporating Machine Learning (ML) algorithms. The proposed system indicates the patient’s current status, which can be accessed by both the patient as well as the doctor through a mobile application developed on NetBeans.