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Sensors 2015, 15(3), 6066-6090; doi:10.3390/s150306066

Data Fault Detection in Medical Sensor Networks

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing 100876, China
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Academic Editor: Leonhard M. Reindl
Received: 20 November 2014 / Revised: 23 February 2015 / Accepted: 27 February 2015 / Published: 12 March 2015
(This article belongs to the Section Sensor Networks)
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Abstract

Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians’ diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren’t changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M). Its mechanism includes: (1) use of a dynamic-local outlier factor (D-LOF) algorithm to identify outlying sensed data vectors; (2) use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3) the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M. View Full-Text
Keywords: fault detection; medical sensor; local outlier factor; fuzzy number fault detection; medical sensor; local outlier factor; fuzzy number
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yang, Y.; Liu, Q.; Gao, Z.; Qiu, X.; Meng, L. Data Fault Detection in Medical Sensor Networks. Sensors 2015, 15, 6066-6090.

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