Power efficient compressive sensing for continuous monitoring of ECG and PPG in a wearable system

We demonstrate an end-to-end system prototype using Compressive Sensing (CS) to power-efficiently compress continuous bio-signals in a wearable body sensor network (BSN). We use a variant of a Binary Permuted Block Diagonal (P-BPBD) matrix encoder and pad the input signal symmetrically to achieve high compression ratios for ECG and PPG signals. In our approach, the gateway dynamically tunes compression parameters to adapt to changing signal sparsity levels during continuous monitoring and transmits them to the sensor node. We show that the power consumed by our encoder on a 32MHz Intel® Curie™ compute module is much lower than that consumed by a standard Wavelet-based (DWT) encoder. We also show how CS can simultaneously de-noise ECG signals during reconstruction. After evaluating multiple reconstruction algorithms, we demonstrate real-time signal recovery via an implementation of the Alternating Direction algorithm on a 500MHz Intel® Edison board, used as a gateway.