  Compressive sensing (CS) is a promising technology for realizing
energy-efficient wireless sensors for long-term health monitoring. In this
paper, we propose a data-driven CS framework that learns signal characteristics
and individual variability from patients' data to significantly enhance CS
performance and noise resilience. This is accomplished by a co-training
approach that optimizes both the sensing matrix and dictionary towards improved
restricted isometry property (RIP) and signal sparsity, respectively.
Experimental results upon ECG signals show that our framework is able to
achieve better reconstruction quality with up to 80% higher compression ratio
(CP) than conventional frameworks based on random sensing matrices and
overcomplete bases. In addition, our framework shows great noise resilience
capability, which tolerates up to 40dB higher noise energy at a CP of 9 times.
