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Energy‐Saving Multisensor Data Sampling and Fusion with Decision‐Making for Monitoring Health Risk Using WBSNs
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-10-01 , DOI: 10.1002/spe.2904
Alaa Shawqi Jaber 1 , Ali Kadhum Idrees 2
Affiliation  

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.

中文翻译:

节能多传感器数据采样与决策融合使用 WBSN 监测健康风险

由于人口老龄化和慢性病的流行,开发足够的系统来监测健康状况的必要性已经增加,从而产生了对利用生物传感器的远程医疗保健系统的需求。本文提出了一种节能的多传感器数据采样和融合决策,用于监测无线身体传感器网络 (WBSN) 中的患者健康风险。该工作包括三个步骤:节能采样率适应、多传感器数据融合和决策。采样在每个生物传感器中执行,它根据局部风险和全局风险调整其速率,其中全局风险在协调器处计算,然后数据融合。最后,根据患者的风险水平做出决定。这些函数的处理可以根据每个生物传感器的动态风险水平实时采用生物传感器采样率,并在检测到紧急情况时做出相应的决定。使用实际健康数据集评估建议方法的性能,并将其某些方面与现有方法进行比较,例如数据减少和能源消耗率。获得的结果表明收集的数据量有所减少,因此在保持数据准确性和完整性的同时显着节省了能源。此外,通过预警评分系统在协调员级别呈现数据融合模型,评估了患者的健康状况,并在发现紧急情况时做出了适当的决定。
更新日期:2020-10-01
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