Energy‐Saving Multisensor Data Sampling and Fusion with Decision‐Making for Monitoring Health Risk Using WBSNs

Abstract

The necessity of developing sufficient systems to monitor health conditions has increased due to the aging of the population and the prevalence of chronic diseases, creating a demand for remote health care systems that make use of biosensors. This article proposes an energy‐saving multisensor data sampling and fusion with decision‐making for the monitoring of patient health risk in wireless body sensor networks (WBSNs). The work consists of three steps: energy‐efficient sampling rate adaptation, multisensor data fusion, and decision‐making. The sampling is performed in each biosensor and it adapts its rate based on the local risk and the global risk in which global risk computed at the coordinator, where the data is fused afterward. Finally, decisions are made according to the risk level of the patient. The processing of these functions enables in real‐time the adoption of the biosensor sampling rates based on the dynamic risk level of each biosensor, and a corresponding decision is made whenever an emergency is detected. The performance of the suggested approach is evaluated using actual health datasets, and some of its aspects are put into comparison with an existing approach, such as the data reducing and energy‐consuming rates. The acquired results illustrate a decrease in the volume of gathered data, thus a significant energy saving has been made while preserving data accuracy and integrity. Moreover, presenting a data fusing model at the coordinator level by means of an early warning score system has assessed the health condition of patients and took an appropriate decision when detecting emergencies.

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