@misc{10481/80078,
year = {2023},
month = {1},
url = {https://hdl.handle.net/10481/80078},
abstract = {Vital signs estimation provides valuable information about an individual’s overall health
status. Gathering such information usually requires wearable devices or privacy-invasive settings.
In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction
while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily
adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract
respiration information using a 60 GHz frequency-modulated continuous wave radar. With few
training examples, episodic optimization-based learning allows for generalization to new individuals.
Episodically, a convolutional variational autoencoder learns how to map the processed radar data
to a reference signal, generating a constrained latent space to the central respiration frequency.
Moreover, autocorrelation over recorded radar data time assesses the information corruption due to
subject motions. The model learning procedure and breathing prediction are adjusted by exploiting
the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an
adaptation time of less than one and two seconds for one to five training examples, respectively. The
suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings
with little user motion.},
organization = {ITEA3 Unleash Potentials in Simulation
(UPSIM) project (N°19006) German Federal Ministry of Education and Research
(BMBF)},
organization = {Austrian Research Promotion Agency (FFG)},
organization = {Rijksdienst voor Ondernemend Nederland
(Rvo)},
organization = {Innovation Fund Denmark (IFD)},
publisher = {MDPI},
keywords = {Vital sign sensing},
keywords = {Respiration signal},
keywords = {Artificial neural networks},
keywords = {Meta-learning},
keywords = {Radar},
keywords = {FMCW},
keywords = {Few-shot learning},
keywords = {Autocorrelation},
keywords = {Variational Autoencoder},
keywords = {Signal processing},
title = {Few-Shot User-Adaptable Radar-Based Breath Signal Sensing},
doi = {10.3390/s23020804},
author = {Mauro, Gianfranco and Pegalajar Cuéllar, Manuel and Morales Santos, Diego Pedro},
}