@inproceedings{948,
  author = {Zongxing Xie and Ava Nederlander and Isac Park and Fan Ye},
  title = {Poster: Quantifying Signal Quality Using Autoencoder for Robust RF-based Respiration Monitoring},
  abstract = {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. We propose RF-Q for robust RF-based respiration monitoring, using self-supervised learning with an autoencoder (AE) neural network to quantify the quality of respiratory signal based on the residual between the original and reconstructed sig- nals. We demonstrate that, by simply quantifying the signal quality with AE for weighted estimation we can boost the end-to-end (e2e) respiration monitoring accuracy by an improvement ratio of 2.75 compared to a baseline.},
  year = {2023},
  journal = {ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies},
  month = {01},
  url = {https://par.nsf.gov/biblio/10439763},
}