Energy-Aware Bio-signal Compressed Sensing Reconstruction on the WBSN-gateway

Technology scaling enables today the design of ultra-low power wearable bio-sensors for continuous vital signs monitoring or wellness applications. Such bio-sensing nodes are typically integrated in Wireless Body Sensor Network (WBSN) to acquire and process biomedical signals, e.g., Electrocardiogram (ECG), and transmit them to the WBSN gateway, e.g., smartphone, for online reconstruction or features extraction. Both bio-sensing node and gateway are battery powered devices, although they show very different autonomy requirements (weeks versus days). The rakeness-based Compressed Sensing (CS) proved to outperform standard CS, achieving a higher compression for the same quality level, therefore reducing the transmission costs in the node. However, most of the research focus has been on the efficiency of the node, neglecting the energy cost of the CS decoder. In this work, we evaluate the energy cost and real-time reconstruction feasibility on the gateway, considering different signal reconstruction algorithms running on a heterogeneous mobile SoC based on the ARM big.LITTLETM architecture. The experimental results show that it is not always possible to obtain the theoretical QoS under real-time constraints. Moreover, the standard CS does not satisfy real-time constraints, while the rakeness enables different QoS-energy trade-offs. Finally, we show that in the optimal setup (OMP, n=128) heterogeneous architectures make the CS decoding task suitable for wearable devices oriented to long-term ECG monitoring.