Design and analysis of low-power body area networks based on biomedical signals

摘要

A body area network (BAN) as one branch of Sensor Networks, is an interdisciplinary area which holds great promises for revolutionising the current health care systems. BAN combines the real-time updating of biomedical data with the continuous and dynamic health care monitoring closely. A number of intelligence biomedical sensors can be integrated into a wireless BAN system, and the system can be used for prevention, diagnosis and timely treatment of various medical conditions. In this article, we propose a data fusion technique for a BAN based on biomedical signals. This proposed solution is of much lower complexity than conventional techniques and hence can significantly reduce the power consumption in the BAN. The technology is carried out by removing redundant and unnecessary sample information and shifting a large portion of processing and control loads to the remote control centre in an asymmetric manner. This approach not only reduces the power consumption of biosensor nodes in a BAN, but also ensures the integrity of the biomedical information. In addition, we present a self-designed distributed time-space correlation compressive sensing model and propose an efficient algorithm based on biomedical signals. Simulation results show that the proposed algorithm can not only reconstruct the original signal with high accuracy and but also achieve significant reduction in power consumption.