  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.
