Self-Adaptive Data Collection and Fusion for Health Monitoring Based on Body Sensor Networks

In the past few years, wireless body sensor networks (WBSNs) emerged as a low-cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measurement and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a biosensor data management framework, starting from data collection to decision making. First, we propose an adaptive data collection approach on the biosensor node level. This approach uses an early warning score system to optimize data transmission and estimates in real time the sensing frequency. Second, we present a data fusion model on the coordinator level using a decision matrix and fuzzy set theory. To evaluate our approach, we conducted multiple series of simulations on real sensor data. The results show that our approach reduces the amount of collected data, while maintaining data integrity. In addition, we show the impact of sampling and filtering data on the accuracy of the taken decisions and compare our data fusion approach with a basic decision tree algorithm.