Quantized Compressive Sensing for Low-Power Data Compression and Wireless Telemonitoring

In low-power wireless telemonitoring, physiological signals must be compressed before transmission to extend battery life. In this paper, we propose a two-stage data compressor based on quantized compressive sensing (QCS), where signals are first compressed by compressive sensing with a 50% compression ratio and then quantized with 2 bits per measurement. We also develop a reconstruction algorithm, called Bayesian de quantization (BDQ), to recover signals from the quantized compressed measurements. This algorithm exploits both the model of quantization errors and the correlated structure of physiological signals, which improves the quality of recovery. We validate the proposed data compressor and the recovery algorithm on wrist-type photoplethysmography data. This data are used to estimate the heart rate during fitness training. Results show that an average estimation error of 2.596 beats per minute is achieved using QCS and BDQ. This accuracy approaches the performance on non-compressed data, but we transmit n bits instead of n samples, which is a substantial improvement for low-power telemonitoring.