Overview of Efficient Compressive Sensing Reconstruction Engines for E-Health Applications

Compressive sensing (CS) is an emerging technique for measuring sparse signals at the sub-Nyquist rate. It enables new reduced-complexity designs for sensor nodes and helps reduce overall transmission power in wireless sensor network. For on-site analysis in E-health applications, mobile data aggregator that performs real-time signal reconstruction at the body side is necessary. However, with high computational complexity of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. As a result, CS reconstruction engine was proposed to bring the signal intelligence closer to the user for timely prediction and decision-making. To design a highly efficient CS reconstruction engine on mobile platforms, two major design issues should be considered, including 1) throughput and energy efficiency, and 2) reconstruction quality. In this paper, the design challenges and concepts of CS reconstruction engine are introduced. In addition, the representative related works are reviewed and summarized. Finally, we conclude the paper and point out the future works of CS reconstruction engine.