Few-Shot User-Adaptable Radar-Based Breath Signal Sensing Mauro, Gianfranco Pegalajar Cuéllar, Manuel Morales Santos, Diego Pedro Vital sign sensing Respiration signal Artificial neural networks Meta-learning Radar FMCW Few-shot learning Autocorrelation Variational Autoencoder Signal processing 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. 2023-02-20T11:36:50Z 2023-02-20T11:36:50Z 2023-01-10 journal article Mauro, G... [et al.]. Few-Shot User-Adaptable Radar-Based Breath Signal Sensing. Sensors 2023, 23, 804. [https://doi.org/10.3390/s23020804] https://hdl.handle.net/10481/80078 10.3390/s23020804 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI