arXiv 2109.145462021-09-27eess.SPcs.LGcs.NI

一种基于无线体域网的节能健康监测方法

An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks

Indian Institute of Technology Indore（印度理工学院印多尔分校）；University of the Witwatersrand（金山大学）

Seemandhar Jain, Prarthi Jain, Prabhat K. Upadhyay, Jules M. Moualeu, Abhishek Srivastava

备注：: 23 pages, 18 figures. (Full Abstract : https://seemandhar.herokuapp.com/wban)

摘要：

无线体域网（WBAN）包括皮下植入或放置在身体表面附近的传感器网络，并且便于连续监测患者的健康参数。涉及WBAN的研究努力针对将检测到的参数有效地传输到本地处理单元（LPU，通常是移动终端）以及在LPU或后端云处分析参数。WBAN中的一个重要关注点是WBAN节点的轻量性质以及保存它们的能量的需要。对于不能充电或定期更换的皮下植入节点尤其如此。节能方面的工作主要旨在优化信号的路由，以最大限度地减少能源消耗。本文提出了一种简单而又创新的节能和检测报警健康状态的方法。通过两层方法确保能量节约，其中第一层消除在感测节点的位置处的"不感兴趣的"健康参数读数，并且防止这些读数通过WBAN被传输到LPU。如果读数与前一次读数的偏差非常小，并且没有提供关于患者健康的新见解，则将其归类为不感兴趣。除此之外，还可以消除可能的传感器故障引起的错误读数。这些消除是在传感器的位置使用算法，是轻到足以有效地在传感器节点的资源极其有限的环境中发挥作用。通过实验，我们注意到，这消除并因此减少了需要传输到LPU的读数的约90%，从而显著节省了能源。此外，这些算法在这样的约束环境中的正常运作，确认和验证了硬件仿真设置。第二层评估包括在LPU处提出的异常检测模型，该异常检测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外，该模型还可以在LPU的资源受限环境中工作，并且无需将数据传输到后端云，从而确保进一步节省能源。该模型的异常检测能力进行了验证，使用从医院的重症监护室的数据，并被证明是优于其他异常检测技术。

英文摘要：

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 paper, 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. A reading is categorised as uninteresting if it deviates very slightly from its immediately preceding reading and does not provide new insight on the patient's well being. In addition to this, readings that are faulty and emanate from possible sensor malfunctions are also eliminated. These eliminations are done at the site of the sensor using algorithms that are light enough to effectively function in the extremely resource-constrained environments of the sensor nodes. We notice, through experiments, that this eliminates and thus reduces around 90% of the readings that need to be transmitted to the LPU leading to significant energy savings. Furthermore, the proper functioning of these algorithms in such constrained environments is confirmed and validated over a hardware simulation set up. 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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