Wearable Technology and Body Sensor Networks for Remote Healthcare Monitoring

This study presents a novel AI-driven framework for remote healthcare monitoring, integrating multi-sensor data fusion, real-time health risk assessment, and adaptive intervention strategies to enhance wearable technology and body sensor networks. The proposed methodology dynamically optimizes sensor reliability by computing reliability factors, adjusting weights through an adaptive mechanism, and applying noise reduction techniques to ensure high-fidelity data integration. The risk assessment model employs an entropy-based classification system to categorize health risks into distinct levels—Low, Moderate, and High—allowing for precise and timely interventions. Additionally, the intervention module dynamically adjusts health responses using AI-driven feedback loops, ensuring continuous system refinement and personalized healthcare delivery. The proposed method demonstrates high accuracy in sensor data fusion (96.7%), strong reliability in health risk assessment (95.8%), and efficient adaptive learning performance (94.2%), surpassing conventional models. It enhances real-time processing efficiency (180 ms) and extends battery life (72 hours), improving the usability of wearable healthcare devices. The system also exhibits robust anomaly detection (96.0%), resilience against sensor failures (93.4%), and high user compliance (91.8%), ensuring seamless integration into healthcare routines. Additionally, data transmission latency is minimized to 120 ms, making remote patient monitoring more responsive and effective. These advancements establish the proposed method as a scalable, secure, and adaptive framework for real-time healthcare monitoring. By leveraging AI-driven decision-making, multi-sensor integration, and continuous learning mechanisms, this approach significantly improves patient outcomes, system robustness, and healthcare efficiency. It serves as a foundation for the future development of intelligent, data-driven, and privacy-preserving healthcare systems.