AI-Integrated Wearable IoT Devices for Analyzing Diabetic Walking Patterns Using Neural Networks

Complications from diabetes, such as diabetic neuropathy, may greatly limit a person's range of mobility and walking. To address this problem, we provide an innovative system that tracks and evaluates diabetes patients' walking patterns using wearable Internet of Things (IoT) sensors connected with artificial intelligence (AI). Wearable sensors integrated into our system collect stride length, gait velocity, and pressure distribution in real time. Subtle variations and patterns may indicate the start of diabetes issues, and this data is evaluated using advanced neural network methods. Patterns connected to neuropathy and other walking defects, particularly those of diabetes patients, are provided to the neural network models. The AI technology enables early intervention and personalized recommendations by providing quick feedback and useful data. Patients may continually monitor their walking patterns by incorporating this technology into their everyday lives, which may help to avoid serious consequences and improve their quality of life. This research provides a realistic solution for continuously monitoring and controlling diabetic walking health by integrating wearable IoT technology with neural networks, which can improve diabetic management.