Simple and Efficient Compressed Sensing Encoder for Wireless Body Area Network

Compressed sensing (CS) is an emerging signal processing technique that enables sub-Nyquist measurement of signals having sparse representations in certain bases. Since most physiological signals treated within a wireless body area network (WBAN) are sparse, CS can be applied to WBANs to reduce the number of measurements and minimize the energy consumption of the sensor nodes. In this paper, we propose a simple and efficient CS encoder device used to measure signals within sensor nodes of a WBAN. A digital and an analog models of the proposed CS encoder are presented. As the CS encoder and decoder are tightly coupled, a model of the overall acquisition chain is required in the first stages of development and validation. To do this, we propose a virtual prototyping of the system with SystemC-AMS. A SPICE model and a hardware prototype of the proposed CS encoder are also presented. The simulation results of both models show that the proposed encoder was able to compressively measure an electrocardiogram (ECG) and an electroencephalogram signals with compression ratios of 6:1 and 4:1, respectively, which save 82.9% and 75% of the energy consumption of transceivers. The experiment results were consistent with those of the model and show that the hardware prototype was able to compressively measure an ECG signal with a compression ratio of 8:1. Comparison with a random demodulator (RD) was carried out and shows that the proposed encoder outperformed RD in terms of compression ratio and reconstruction quality.