An Energy-Efficient Compressive Sensing Framework Incorporating Online Dictionary Learning for Long-term Wireless Health Monitoring

TL;DR

This paper introduces an energy-efficient compressive sensing framework with online dictionary learning for wireless health monitoring, significantly reducing data size and hardware costs in WBANs.

Contribution

It combines compressive sensing with online dictionary learning, improving compression ratios and offloading pre-processing to reduce hardware costs in wireless health monitoring.

Findings

Achieves 2-4x better compression ratios than prior methods.

Reduces hardware costs by offloading pre-processing to server.

Potentially applicable to various physiological signals.

Abstract

Wireless body area network (WBAN) is emerging in the mobile healthcare area to replace the traditional wire-connected monitoring devices. As wireless data transmission dominates power cost of sensor nodes, it is beneficial to reduce the data size without much information loss. Compressive sensing (CS) is a perfect candidate to achieve this goal compared to existing compression techniques. In this paper, we proposed a general framework that utilize CS and online dictionary learning (ODL) together. The learned dictionary carries individual characteristics of the original signal, under which the signal has an even sparser representation compared to pre-determined dictionaries. As a consequence, the compression ratio is effectively improved by 2-4x comparing to prior works. Besides, the proposed framework offloads pre-processing from sensor nodes to the server node prior to dictionary learning, providing further reduction in hardware costs. As it is data driven, the proposed framework has the potential to be used with a wide range of physiological signals.