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.
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