Energy-efficient IoT-based tracking and management system for patients with cognitive diseases

Abstract

The proliferation of Internet of Things (IoT) technologies has created new opportunities for healthcare applications, particularly when it comes to tracking and assisting people with cognitive diseases like autism and Alzheimer’s. In order to meet the unique requirements of individuals with Alzheimer’s and autism, this article suggests a novel energy efficient IoT tracking model that ensures ongoing accurate and reliable monitoring. To develop a complete tracking system, the suggested model combines machine learning algorithms with energy-efficient sensors. Optimizing energy efficiency receives special attention because continuous patient monitoring depends on the device operation under different environmental conditions. This methodology optimizes the energy efficiency of the system using a 1-Deep Convolutional Neural Network (DCNN) and Lagrange optimization algorithms. The primary objective is to ascertain the ideal distance required for sending emergency signal for patients’ wearable IoT devices to travel in the event that their medical problems cause them to become misplaced. The proposed method aims to enhance the overall performance of the communication system by integrating mathematical optimization principles with modern deep learning techniques. This will contribute to the development of more dependable and efficient emergency response mechanisms. This research proposes an energy-efficient IoT tracking methodology that makes a significant contribution to the healthcare technology space. Through addressing the particular difficulties that patients with Autism and Alzheimer’s disease present, the model provides a viable way to improve care, encourage independence, and lessen the workload for carers in an energy-efficient way.