Dinesh Tarasing Rathod Rahul M Mulajkar Vaishali Mangesh Dhede

This paper presents an enhanced real-time, adaptive wearable health monitoring system that expands upon earlier work by incorporating advanced predictive analytics, improved sensor integration, and broader real-world testing scenarios. The system utilizes an updated suite of wearable sensors to monitor physiological parameters such as ECG, PPG, and respiratory signals, while leveraging advanced noise filtering, feature extraction, and dynamic anomaly detection algorithms. New methodologies, including multi-modal sensor fusion and hybrid machine learning models, improve the system’s accuracy and scalability. Extensive validation using real-world data highlights the system’s ability to provide reliable and timely health alerts, with expanded functionality for chronic disease management and personalized health tracking. These enhancements address limitations in prior implementations, offering a more comprehensive and scalable approach to real-time health monitoring. The findings underscore the potential of adaptive health systems to transform preventive and personalized healthcare..

https://doi.org/10.58357/jedcs.2025.v4.i1.17