Revolutionizing healthcare monitoring: An adaptive wearable framework for realtime decision support

This paper presents a comprehensive methodology for wearable health monitoring systems, emphasizing the Wearable Health Monitoring and Feedback Algorithm (WHMFA). By leveraging advanced data preprocessing techniques, real- time health assessments, and adaptive learning mechanisms, the WHMFA ensures accurate health status classifications, personalized feedback, and robust data security. The system incorporates noise filtering, machine learning-based predictions, and encrypted data transmission, ensuring reliability and privacy in healthcare monitoring. Simulation results demonstrate superior performance in accuracy, latency, and resource efficiency compared to existing systems, showcasing WHMFA's potential for enhancing patient outcomes and promoting real-time health management. This work addresses critical challenges in sensor reliability, privacy, and adaptability, contributing to the advancement of wearable health technologies.