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Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition

Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolution...

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Detalles Bibliográficos
Autores principales: Kempfle, Jochen, Van Laerhoven, Kristof
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412468/
https://www.ncbi.nlm.nih.gov/pubmed/32668594
http://dx.doi.org/10.3390/s20143884
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author Kempfle, Jochen
Van Laerhoven, Kristof
author_facet Kempfle, Jochen
Van Laerhoven, Kristof
author_sort Kempfle, Jochen
collection PubMed
description Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolutions have now enabled a novel use of depth cameras that facilitate more fine-grained activity descriptors: The remote detection of a person’s breathing by picking up the small distance changes from the user’s chest over time. We propose in this work a novel method to model chest elevation to robustly monitor a user’s respiration, whenever users are sitting or standing, and facing the camera. The method is robust to users occasionally blocking their torso region and is able to provide meaningful breathing features to allow classification in activity recognition tasks. We illustrate that with this method, with specific activities such as paced-breathing meditating, performing breathing exercises, or post-exercise recovery, our model delivers a breathing accuracy that matches that of a commercial respiration chest monitor belt. Results show that the breathing rate can be detected with our method at an accuracy of 92 to 97% from a distance of two metres, outperforming state-of-the-art depth imagining methods especially for non-sedentary persons, and allowing separation of activities in respiration-derived features space.
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spelling pubmed-74124682020-08-26 Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition Kempfle, Jochen Van Laerhoven, Kristof Sensors (Basel) Article Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolutions have now enabled a novel use of depth cameras that facilitate more fine-grained activity descriptors: The remote detection of a person’s breathing by picking up the small distance changes from the user’s chest over time. We propose in this work a novel method to model chest elevation to robustly monitor a user’s respiration, whenever users are sitting or standing, and facing the camera. The method is robust to users occasionally blocking their torso region and is able to provide meaningful breathing features to allow classification in activity recognition tasks. We illustrate that with this method, with specific activities such as paced-breathing meditating, performing breathing exercises, or post-exercise recovery, our model delivers a breathing accuracy that matches that of a commercial respiration chest monitor belt. Results show that the breathing rate can be detected with our method at an accuracy of 92 to 97% from a distance of two metres, outperforming state-of-the-art depth imagining methods especially for non-sedentary persons, and allowing separation of activities in respiration-derived features space. MDPI 2020-07-13 /pmc/articles/PMC7412468/ /pubmed/32668594 http://dx.doi.org/10.3390/s20143884 Text en © 2020 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
Kempfle, Jochen
Van Laerhoven, Kristof
Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition
title Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition
title_full Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition
title_fullStr Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition
title_full_unstemmed Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition
title_short Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition
title_sort towards breathing as a sensing modality in depth-based activity recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412468/
https://www.ncbi.nlm.nih.gov/pubmed/32668594
http://dx.doi.org/10.3390/s20143884
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