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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 602358, 13 pages
http://dx.doi.org/10.1155/2012/602358
Research Article

MPD-Model: A Distributed Multipreference-Driven Data Fusion Model and Its Application in a WSNs-Based Healthcare Monitoring System

1Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
2Department of Computer Science and Engineering, Yanshan University, Qinhuangdao 066004, China
3Graduate University of Chinese Academy of Sciences, Beijing 100190, China

Received 6 September 2012; Accepted 31 October 2012

Academic Editor: Fu Xiao

Copyright © 2012 Jibing Gong et al. 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.

Abstract

We first propose an MPD-Model, a novel distributed multipreference-driven data fusion model for WSNs. Here, preferences are looked as the core elements of collaboration mechanism in a data fusion procedure. We then present MFA, a distributed multi-preference feature-level fusion algorithm based on weighted average method. Next, to implement feature extraction of wrist-pulse data, we propose FEA, a light-weight adaptive feature extraction algorithm for time series sensed data. Simultaneously, we design TFD-Pattern that is a unique human pulse pattern. Based on historical data, we propose an SVM-based algorithm for health status detection tasks. Finally, we implement the proposed methods in a real wearable healthcare monitoring system which had been previously developed in-house. We validate the proposed methods using real-world data sets with 2046 pulse samples. Experimental results show that the proposed methods outperform the baseline methods, and the proposed MPD-Model is reasonable and effective.