The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health of the monitored patient. However, the transferred data are vulnerable to various sources of interference, sensor faults, measurement faults, injection and alteration by malicious attackers, etc. In this article, we propose a change point detection model based on a Markov chain for centralized anomaly detection in WBANs. The model is derived from the Root Mean Square Error (RMSE) between the forecasted and measured values for whole attributes. The RMSE transforms the monitored attributes into a univariate times series which is divided into overlapping sliding window. The joint probability of the sequence of RMSE values in each sliding window is calculated to decide whether a change has occurred or not. When an effective change is detected over k consecutive windows, the number of deviated attributes is used to distinguish faulty measurements from a health emergency. We apply our proposed approach on real physiological data from the Physionet database and compare it with existing approaches. Our experimental results prove the effectiveness of our proposed approach, as it achieves high detection accuracy with a low false alarm rate (5.2%).

中文翻译：

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用于安全健康监测的无线人体区域网络异常检测的马尔可夫模型


在医疗保健中使用无线体域网 (WBAN) 进行普遍监测可以改善患者的生活，并使他们能够在受到监测的同时完成日常生活活动。将各种非侵入性传感器放置在皮肤上以监测多种生理属性，并将测量的数据无线传输到中央处理单元以检测受监测患者的健康变化。然而，传输的数据容易受到各种干扰源、传感器故障、测量故障、恶意攻击者的注入和篡改等。在本文中，我们提出了一种基于马尔可夫链的变化点检测模型，用于 WBAN 中的集中异常检测。该模型源自整个属性的预测值和测量值之间的均方根误差 (RMSE)。 RMSE 将监测的属性转换为单变量时间序列，该时间序列被划分为重叠的滑动窗口。计算每个滑动窗口中 RMSE 值序列的联合概率来决定是否发生变化。当在 k 个连续窗口中检测到有效变化时，偏差属性的数量用于区分错误测量和健康紧急情况。我们将我们提出的方法应用于 Physionet 数据库的真实生理数据，并将其与现有方法进行比较。我们的实验结果证明了我们提出的方法的有效性，因为它实现了高检测精度和低误报率（5.2％）。