基于无线体域网络的节能健康监测方法

Seemandhar Jain, Prarthi Jain, P. K. Upadhyay, J. M. Moualeu, Abhishek Srivastava
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引用次数: 2

摘要

无线身体区域网络(wban)包括皮下植入或放置在体表附近的传感器网络,并有助于对患者的健康参数进行连续监测。涉及WBAN的研究工作旨在将检测到的参数有效地传输到本地处理单元(LPU,通常是移动设备),并在LPU或后端云上分析参数。WBAN中一个重要的问题是WBAN节点的轻量特性和节省其能量的需要。对于不能充电或定期更换的皮下植入淋巴结尤其如此。节能工作的主要目的是优化信号的路由,以尽量减少能量消耗。本文提出了一种简单而创新的节能和报警健康状态检测方法。通过两层方法确保节能,其中第一层消除了传感节点站点的“无趣”健康参数读数,并防止这些读数通过WBAN传输到LPU。第二层评估包括提议的LPU异常检测模型,该模型能够从流式健康参数读数中识别异常,并指示不良医疗状况。除了能够处理流数据之外,该模型还可以在LPU的资源受限环境中工作,并且不需要将数据传输到后端云,从而确保进一步节能。该模型的异常检测能力通过使用医院重症监护病房的可用数据进行验证,并被证明优于其他异常检测技术。
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An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks
Wireless Body Area Networks (WBANs) comprise a network of sensors subcutaneously implanted or placed near the body surface and facilitate continuous monitoring of health parameters of a patient. Research endeavours involving WBAN are directed towards effective transmission of detected parameters to a Local Processing Unit (LPU, usually a mobile device) and analysis of the parameters at the LPU or a back-end cloud. An important concern in WBAN is the lightweight nature of WBAN nodes and the need to conserve their energy. This is especially true for subcutaneously implanted nodes that cannot be recharged or regularly replaced. Work in energy conservation is mostly aimed at optimising the routing of signals to minimise energy expended. In this article, a simple yet innovative approach to energy conservation and detection of alarming health status is proposed. Energy conservation is ensured through a two-tier approach wherein the first tier eliminates “uninteresting” health parameter readings at the site of a sensing node and prevents these from being transmitted across the WBAN to the LPU. The second tier of assessment includes a proposed anomaly detection model at the LPU that is capable of identifying anomalies from streaming health parameter readings and indicates an adverse medical condition. In addition to being able to handle streaming data, the model works within the resource-constrained environments of an LPU and eliminates the need of transmitting the data to a back-end cloud, ensuring further energy savings. The anomaly detection capability of the model is validated using data available from the critical care units of hospitals and is shown to be superior to other anomaly detection techniques.
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