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dc.contributor.authorMauro, Gianfranco
dc.contributor.authorPegalajar Cuéllar, Manuel 
dc.contributor.authorMorales Santos, Diego Pedro 
dc.date.accessioned2023-02-20T11:36:50Z
dc.date.available2023-02-20T11:36:50Z
dc.date.issued2023-01-10
dc.identifier.citationMauro, G... [et al.]. Few-Shot User-Adaptable Radar-Based Breath Signal Sensing. Sensors 2023, 23, 804. [https://doi.org/10.3390/s23020804]es_ES
dc.identifier.urihttps://hdl.handle.net/10481/80078
dc.description.abstractVital 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.es_ES
dc.description.sponsorshipITEA3 Unleash Potentials in Simulation (UPSIM) project (N°19006) German Federal Ministry of Education and Research (BMBF)es_ES
dc.description.sponsorshipAustrian Research Promotion Agency (FFG)es_ES
dc.description.sponsorshipRijksdienst voor Ondernemend Nederland (Rvo)es_ES
dc.description.sponsorshipInnovation Fund Denmark (IFD)es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectVital sign sensinges_ES
dc.subjectRespiration signales_ES
dc.subjectArtificial neural networkses_ES
dc.subjectMeta-learninges_ES
dc.subjectRadar es_ES
dc.subjectFMCWes_ES
dc.subjectFew-shot learninges_ES
dc.subjectAutocorrelationes_ES
dc.subjectVariational Autoencoderes_ES
dc.subjectSignal processing es_ES
dc.titleFew-Shot User-Adaptable Radar-Based Breath Signal Sensinges_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.3390/s23020804
dc.type.hasVersionVoRes_ES


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Atribución 4.0 Internacional
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