Boosting Telemedicine Healthcare Assessment Using Internet of Things and Artificial Intelligence for Transforming Alzheimer's Detection

Abstract

The emergence of Internet-of-Things (IoT)-based telemedicine platforms has initiated a new technological age that enables early detection and treatment for remote patients. This is especially vital for serious conditions like Alzheimer's Disease (AD), which involves decreased memory and cognitive impairment that greatly disrupts everyday functioning, requiring prompt medical intervention. The influx of information from automated systems, originating from many sources, has increased complexity and reduced diagnostic precision. This study enhances Telemedicine Healthcare Assessment by integrating IoT and Artificial Intelligence to advance Alzheimer's Detection (THA-AI-AD). The paper outlines the fundamental principles and key elements of multifaceted sensing data acquisition, the design and execution of medical tracking using an AI platform, robotics-driven multifaceted data sensing and gathering, and the long-term acquisition of physiological signals with high comfort through smart clothing. The study presents an innovative technique for the automated identification of AD using Convolutional Neural Networks (CNN). Consisting of two subsystems, one focused on AD detection with an accuracy of 97.12% using CNN and another for AI-based telemedicine therapy, yielding exceptional outcomes.