Few-Shot User-Adaptable Radar-Based Breath Signal Sensing
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...
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| Vydané v: | Sensors (Basel, Switzerland) Ročník 23; číslo 2; s. 804 |
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| Hlavní autori: | , , , , , |
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| Jazyk: | English |
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10.01.2023
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Ott, Julius Morales-Santos, Diego P. De Carlos Diez, Maria Servadei, Lorenzo Cuellar, Manuel P. Mauro, Gianfranco |
| AuthorAffiliation | 2 Department of Electronic and Computer Technology, University of Granada, Avenida de Fuente Nueva s/n, 18071 Granada, Spain 4 Department of Computer Science and Artificial Intelligence, University of Granada, C/. Pdta. Daniel Saucedo Aranda s/n, 18015 Granada, Spain 1 Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany 3 Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany |
| AuthorAffiliation_xml | – name: 4 Department of Computer Science and Artificial Intelligence, University of Granada, C/. Pdta. Daniel Saucedo Aranda s/n, 18015 Granada, Spain – name: 3 Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany – name: 1 Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany – name: 2 Department of Electronic and Computer Technology, University of Granada, Avenida de Fuente Nueva s/n, 18071 Granada, Spain |
| Author_xml | – sequence: 1 givenname: Gianfranco orcidid: 0000-0003-3204-1555 surname: Mauro fullname: Mauro, Gianfranco – sequence: 2 givenname: Maria surname: De Carlos Diez fullname: De Carlos Diez, Maria – sequence: 3 givenname: Julius orcidid: 0000-0001-8259-3070 surname: Ott fullname: Ott, Julius – sequence: 4 givenname: Lorenzo orcidid: 0000-0003-4322-834X surname: Servadei fullname: Servadei, Lorenzo – sequence: 5 givenname: Manuel P. surname: Cuellar fullname: Cuellar, Manuel P. – sequence: 6 givenname: Diego P. orcidid: 0000-0002-3294-8934 surname: Morales-Santos fullname: Morales-Santos, Diego P. |
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| Keywords | artificial neural networks radar signal processing meta-learning respiration signal vital sign sensing FMCW variational autoencoder autocorrelation few-shot learning |
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| SubjectTerms | Algorithms artificial neural networks Corruption FMCW Heart Rate Humans meta-learning Monitoring, Physiologic - methods Privacy Radar Respiration respiration signal Respiratory Rate Sensors Signal Processing, Computer-Assisted vital sign sensing Vital Signs |
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| Title | Few-Shot User-Adaptable Radar-Based Breath Signal Sensing |
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