Transformative Potential IoT Sensor Frameworks for Real-Time Alzheimer's Detection and Monitoring in Elderly Populations

This review paper presents a comprehensive overview of IoT sensor-based frameworks for real-time Alzheimer's disease detection and monitoring in elderly populations. The paper examines the current state of Alzheimer's disease, including its definition, stages, symptoms, and challenges in diagnosis. It explores the importance of early detection and monitoring in improving patient outcomes and highlights the role of IoT sensors in enabling real-time monitoring of physiological parameters, activity levels, and behavior patterns associated with Alzheimer's disease. The paper discusses various applications of IoT sensors in healthcare and disease monitoring, including smart home monitoring systems, wearable sensor-based interventions, and community-based screening programs. It analyzes methodologies, technologies, and outcomes in IoT sensor-based research, emphasizing the integration of machine learning algorithms for real-time analysis of sensor data. Additionally, the paper addresses challenges and future directions in the field, including regulatory and ethical considerations, technical challenges, and potential advancements. Overall, this review paper underscores the transformative potential of IoT sensor-based frameworks in improving early detection, personalized care, and disease management for individuals living with Alzheimer's disease.