Quantifying the benefits of compressed sensing on a WBSN-based real-time biosignal monitor

Technology scaling enables today the design of ultra-low power wearable biosensors for continuous vital signal monitoring or wellness applications. Wireless Body Sensor Networks (WBSN) integrate wearable sensing nodes for an accurate measurement of the desired physiological parameter, e.g. Electrocardiogram (ECG), and a personal gateway for the collection and processing of the data. The diffusion of smartphones enables their use as advanced personal gateways, with the ability to provide user interaction features, connectivity and real-time feedback to the user. Both the sensing node(s) and the smartphone are battery powered and resource-constrained devices, hence energy efficiency is one of the key design goals. In this work, we explore the use of compression techniques to improve the efficiency of a wireless ECG wearable monitor. In the presented system, the wearable node is used for bio-signal acquisition, pre-processing and compression, while a smartphone is used for real-time signal reconstruction. The system aims at medical-grade signal quality and the impact of lossy compression is tested on real signals acquired by the node and its effects are evaluated on systemlevel energy consumption. We analyze performance/energy tradeoffs considering online data compression on the wearable device and real-time reconstruction on the smartphone. Our results show that Compressed Sensing pays off only when the SNR requirement is below 20 dB due to the non-ideal sparsity of ECG signal. We propose a hybrid compression scheme based on CS and under-quantization to address these limitations