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Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach

Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally...

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Detalles Bibliográficos
Autores principales: Slapničar, Gašper, Wang, Wenjin, Luštrek, Mitja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961385/
https://www.ncbi.nlm.nih.gov/pubmed/33800716
http://dx.doi.org/10.3390/s21051836
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author Slapničar, Gašper
Wang, Wenjin
Luštrek, Mitja
author_facet Slapničar, Gašper
Wang, Wenjin
Luštrek, Mitja
author_sort Slapničar, Gašper
collection PubMed
description Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.
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spelling pubmed-79613852021-03-17 Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach Slapničar, Gašper Wang, Wenjin Luštrek, Mitja Sensors (Basel) Article Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use. MDPI 2021-03-06 /pmc/articles/PMC7961385/ /pubmed/33800716 http://dx.doi.org/10.3390/s21051836 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Slapničar, Gašper
Wang, Wenjin
Luštrek, Mitja
Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach
title Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach
title_full Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach
title_fullStr Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach
title_full_unstemmed Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach
title_short Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach
title_sort classification of hemodynamics scenarios from a public radar dataset using a deep learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961385/
https://www.ncbi.nlm.nih.gov/pubmed/33800716
http://dx.doi.org/10.3390/s21051836
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