Design And Study On Low Power Consumption Of Body Area Network Based On Biomedical Signal

The technology of wireless sensor network has permeated into a new generation of the biomedical application field gradually. With the rapid growing of the wireless communication, biological and physiological sensor, digital medical-tech, computer science and low power integrated circuit technique, WSN presents a diversified trend. The wireless sensor network is more applicable to the different range of applications, for example, the remote health monitoring of human body vital signs, the management of growing status of agriculture plants, monitoring of the forest fire and etc. However, as the special nature of medical health care, the transmission delay, quality of service and power consumption of the special wireless communication has more certain requirements, especially in the long time continuous monitoring application.The textual work will focus on the one brench of the wireless sensor network, the BAN(Body Area Network), which is a cross-disciplinary area and medical health monitoring system of the revolutionary work. With the low cost and humanization, BAN combines the real-time updating biomedical data with the continuous and dynamic health care closely. A great deal of intelligence biomedical sensors can be integrated to a wireless BAN system, and the system can be used for prevention, diagnosis and instantaneous caring of sufferer's condition.The thesis presents a solution for the low power consumption problem of BAN based on biomedical signals, and proposes a low complexity and low power consumption data fusion technique specifically for the design of BAN. The technology is carried out by removing redundant and unnecessary sample information and taking a large number of processing and control works to the remote control center in the asymmetric mode, that not only reduces the power consumption of BAN biosensor nodes, but also ensures the integrity of the biomedical information, makes the BAN biochannel data sent to the remote server accurately. In addition, this thesis also raises self-designed distributed time-space correlation compressed sensing models and algorithm based on biomedical signals, with testing and evaluation of the reconstruction accuracy and power consumption etc. The simulation results show that the algorithm has high power efficiences. The distributed orthogonal matching pursuit algorithm is based on the distributed compressed sensing theory, using the synchronous mechanism of time control interval time and take samples down significantly, thus can efficiently decrease the processing, transmission, storage of the biomedical data caused by power consumption.In summary, the principle based on Compressed Sensing, can reconstruct the biomedical signal fairly accurately with high probability from significantly fewer measurements than that suggested by the Nyquist Shannon sampling rate, and the CS paradigm can better balance the measurements and the reconstruction accuracy in the Medical BAN.The above-mentioned works provide a brand-new paradigm for the theory of the biomedical signal processing and BAN, and a new solution for transmission quality of biomedical data and power efficiency, that have certain reference value for the researches and development, such as compressing imaging, channel coding, Analog to Iinformation conversion etc.