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The emerging compressed sensing (CS) holds considerable promise for continuously acquiring biomedical signals in body sensor networks (BSNs), which enables nodes to employ a much lower sampling rate than Nyquist while still able to accurately reconstruct signals. CS-based BSNs are expected to significantly enhance the quality of healthcare and improve the ability of prevention, early diagnosis, and treatment of chronic diseases. However, existing BSNs are still unable to support long-term monitoring in healthcare, as well as providing an energy-efficient low communication burden and inexpensive scheme. Capitalizing on the sparsity of biomedical signals in transfer domains, this paper develops a continuous biomedical signal acquisition system, which explores a sparsification model to find the sparse representation of biomedical signals. The sparsified measurements of signals are wirelessly transmitted to a fusion center through BSNs. Meanwhile, a weighted group sparse reconstruction algorithm is proposed to accurately reconstruct the signals at the fusion center. Simulation results show that, on random sampling over BSN, the proposed group sparse algorithm shows good efficiency, strong stability, and robustness.
Li et al. (Wed,) studied this question.