  In this paper, we propose a novel framework for the smart healthcare system,
where we employ the compressed sensing (CS) and the combination of the
state-of-the-art machine learning based denoiser as well as the alternating
direction of method of multipliers (ADMM) structure. This integration
significantly simplifies the software implementation for the lowcomplexity
encoder, thanks to the modular structure of ADMM. Furthermore, we focus on
detecting fall down actions from image streams. Thus, teh primary purpose of
thus study is to reconstruct the image as visibly clear as possible and hence
it helps the detection step at the trained classifier. For this efficient smart
health monitoring framework, we employ the trained binary convolutional neural
network (CNN) classifier for the fall-action classifier, because this scheme is
a part of surveillance scenario. In this scenario, we deal with the fallimages,
thus, we compress, transmit and reconstruct the fallimages. Experimental
results demonstrate the impacts of network parameters and the significant
performance gain of the proposal compared to traditional methods.
