Wireless body sensor networks (WBSNs) are vital for healthcare applications but face challenges due to limited energy resources and continuous data transmission. This study proposes F \(^2\) multisense, a novel fuzzy data fusion method to optimize energy consumption at two levels: adaptive emergency detection at the node level and dynamic sampling rate adjustment at the coordinator level. At the first level, data transmission is optimized using adaptive local emergency detection and the NEWS system. At the second level, coordinator level, optimization is achieved through a fuzzy system that adaptively determines the sampling rate. Furthermore, the number of sensors aligns with the number of vital signs in the NEWS system, and the samples examined are numerous, demonstrating the completeness of the method. In addition, the dual-layer design of the system supports real-time adaptation and scalability, ensuring robust performance in dynamically changing environments and large-scale deployments. This approach integrates fuzzy logic with the NEWS system, ensuring reliable health assessments without compromising timeliness or accuracy. To evaluate the method, simulations were performed based on real sensor data from the MIMIC-II database. The results demonstrate a 40% reduction in data transmission and a 64% decrease in energy consumption compared to state-of-the-art methods, allowing efficient and precise patient monitoring.
