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

الملخص EN

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.