TY - GEN
T1 - Poster
T2 - 8th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2023
AU - Xie, Zongxing
AU - Nederlander, Ava
AU - Park, Isac
AU - Ye, Fan
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023
Y1 - 2023
N2 - 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 training 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 signals. 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.
AB - 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 training 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 signals. 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.
KW - RF sensing
KW - Vital signs monitoring
KW - autoencoder (AE)
KW - signal quality assessment
KW - signal reconstruction
KW - unsupervised learning
UR - https://www.scopus.com/pages/publications/85167432452
U2 - 10.1145/3580252.3589999
DO - 10.1145/3580252.3589999
M3 - Conference contribution
AN - SCOPUS:85167432452
T3 - Proceedings - 2023 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2023
SP - 187
EP - 188
BT - Proceedings - 2023 IEEE/ACM International Conference on Connected Health
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 June 2023 through 23 June 2023
ER -