Monitoring Respiratory Sounds: Compressed Sensing Reconstruction via OMP on Android Smartphone

We present a novel respiratory sounds monitoring concept based on compressive sensing (CS). Respiratory sounds are streamed from a body-worn sensor node to a smartphone where processing is conducted. CS is used to simultaneously lower sampling frequency on the sensor node and over-the-air data rate. In this study we emphasize compressed sensing reconstruction via orthogonal matching pursuit (OMP) on Android smartphone. Accuracy of the reconstruction and execution speed are investigated using synthetic signals. We demonstrate applicability of the technique in real-time reconstruction of at least 10 components of compressible DCT spectrum of respiratory sounds containing asthmatic wheezing, acquired at 4x lower sampling rate.