Poster: Quantifying Signal Quality Using Autoencoder for Robust RF-based Respiration Monitoring

While radio frequency (RF) based respiration monitoring for at- home health screening is receiving increasing attention, robustness remains an open challenge. In recent work, deep learning (DL) methods have been demonstrated effective in dealing with non- linear issues from multi-path interference to motion disturbance, thus improving the accuracy of RF-based respiration monitoring. However, such DL methods usually require large amounts of train- ing data with intensive manual labeling efforts, and frequently not openly available.