title: Data fault detection in medical sensor networks.
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.
authors.affiliation: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing 100876, China. yyang@bupt.edu.cn.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
authors.affiliation: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing 100876, China. lq_1990@bupt.edu.cn.
authors.forename: Qian
authors.initials: Q
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing 100876, China. gaozhipeng@bupt.edu.cn.
authors.forename: Zhipeng
authors.initials: Z
authors.lastname: Gao
authors.affiliation: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing 100876, China. xsqiu@bupt.edu.cn.
authors.forename: Xuesong
authors.initials: X
authors.lastname: Qiu
authors.affiliation: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing 100876, China. lmmeng@bupt.edu.cn.
authors.forename: Luoming
authors.initials: L
authors.lastname: Meng
mesh_terms: D000465:Algorithms; D015374:Biosensing Techniques / Q000295:instrumentation*; D003198:Computer Simulation; D019544:Equipment Failure Analysis; D006801:Humans; D008962:Models, Theoretical