A Quick Outlier Detection in Wireless Body Area Networks

Abstract

In healthcare, Wireless Body Area Networks (WBANs) are wireless networks of heterogeneous wearable medical computing devices that enable remote monitoring of a patient's health status, physiological monitoring of vital signs. An important aspect in the design and development of online health monitoring of such WBAN is the speed and accuracy of responses. Immediate response to the changes in the health condition of a patient rescue lives. Faulty measurement signal false alarm and create unusual intervention over healthcare personnel, which makes the online health monitoring system unreliable. This study uses to combine Median Absolute Deviation (MAD) outlier detection method along with Majority Voting (MV) algorithm, the MAD for abnormal data detection and MV algorithm to identify and signal false alarm. The MV is activated only when the MAD detect temporal outliers. A large real-world dataset are used to test our approach and results for this method are promising. We also use synthetically generated data to test the rate of false alarm and compare its performance with other similar algorithms and the proposed approach outperforms two similar outlier detection algorithms.