Energy-Efficient AI Algorithms for Real-Time Health Monitoring in IoT Systems

Abstract

In recent years, the integration of Artificial Intelligence (AI) in healthcare has shown significant promise in enhancing patient monitoring and delivering personalized healthcare solutions. This paper presents the design and implementation of an AI-driven health monitoring system utilizing a Raspberry Pi platform. The system continuously monitors vital signs such as heart rate, temperature, and ECG, using sensors like the AD8232. By leveraging patient health history, the AI component provides tailored health advice, predicts potential health issues, and suggests preventive measures. Furthermore, the system includes an automatic fall detection feature, which triggers an emergency call on an iOS device in the event of a free fall, ensuring immediate assistance. The AI algorithms are trained on historical patient data to recognize patterns indicative of various health conditions, thereby enabling proactive health management. This research highlights the efficacy of combining AI with affordable hardware solutions to create a robust, real-time health monitoring system, ultimately aiming to improve patient outcomes and reduce the burden on healthcare systems.