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.
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A Study of Practical Radar-based Nighttime Respiration Monitoring at Home
Radar-based solutions support practical and longi-
tudinal respiration monitoring owing to their non-invasive nature.
Nighttime respiration monitoring at home provides rich and high-
quality data, mostly free of motion disturbances because the user
is quasi-stationary during sleep, and 6-8 hours per day rather
than tens of minutes, promising for longitudinal studies. However,
most existing work was conducted in laboratory environments for
short periods, thus the environment, user motions, and postures
can differ significantly from those in real homes. To understand
how to obtain quality, overnight respiration data in real homes,
we conduct a thorough experimental study with 6 participants of
various sleep postures over 9 nights in 4 real-home testbeds, each
configured with 3–4 sensors around the bed. We first compare the
performance among four typical sensor placements around the
bed to understand which is the optimal location for high quality
data. Then we explore methods to track range bins with high
quality signals as occasional user motions change the distance
thus signal qualities, and different aspects of amplitude and phase
data to further improve the signal quality using metrics of the
periodicity-to-noise ratio (PNR) and end-to-end (e2e) accuracy.
The experiments demonstrate that the sensor placement is a
vital factor, and the bedside is an optimal choice considering
both accuracy and ease of deployment (2.65 bpm error at 80
percentile), also consistent among four typical sleep postures.
We also observe that, a proper range bin selection method can
improve the PNR by 2 dB at 75-percentile, and e2e accuracy by
0.9 bpm at 80-percentile. Both amplitude and phase data have
comparable e2e accuracy, while phase is more sensitive to motions
thus suitable for nighttime movement detection. Based on these
discoveries, we develop a few simple practical guidelines useful
for the community to achieve high quality, longitudinal home-
based overnight respiration monitoring.
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- Award ID(s):
- 1951880
- NSF-PAR ID:
- 10439719
- Date Published:
- Journal Name:
- IEEE Radar conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Continuous monitoring of respiration provides invaluable insights about health status management (e.g., the progression or recovery of diseases). Recent advancements in radio frequency (RF) technologies show promise for continuous respiration monitoring by virtue of their non-invasive nature, and preferred over wearable solutions that require frequent charging and continuous wearing. However, RF signals are susceptible to large body movements, which are inevitable in real life, challenging the robustness of respiration monitoring. While many existing methods have been proposed to achieve robust RF-based respiration monitoring, their reliance on supervised data limits their potential for broad applicability. In this context, we propose, RF-Q, an unsupervised/self-supervised model to achieve signal quality assessment and quality-aware estimation for robust RF-based respiration monitoring. RF-Q uses the recon- struction error of an autoencoder (AE) neural network to quantify the quality of respiratory information in RF signals without the need for data labeling. With the combination of the quantified sig- nal quality and reconstructed signal in a weighted fusion, we are able to achieve improved robustness of RF respiration monitor- ing. We demonstrate that, instead of applying sophisticated models devised with respective expertise using a considerable amount of labeled data, by just quantifying the signal quality in an unsupervised manner we can significantly boost the average end-to-end (e2e) respiratory rate estimation accuracy of a baseline by an improvement ratio of 2.75, higher than the gain of 1.94 achieved by a supervised baseline method that excludes distorted data.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RadarConf2351548.2023.10149560
