Breathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods

As depth cameras have gotten smaller, more affordable, and more precise, they have also emerged as a promising sensor in ubiquitous systems, particularly for detecting objects, scenes, and persons. This article sets out to systematically evaluate how suitable depth data can be for picking up users’...

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Autores principales:	Jochen Kempfle, Kristof Van Laerhoven
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	respiration sensing depth imaging rgb-d depth-based breathing estimation remote respiration measurement Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/76d6100f042d49ea986636e49f137044
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id	oai:doaj.org-article:76d6100f042d49ea986636e49f137044
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spelling	oai:doaj.org-article:76d6100f042d49ea986636e49f1370442021-12-02T00:37:11ZBreathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods2624-989810.3389/fcomp.2021.757277https://doaj.org/article/76d6100f042d49ea986636e49f1370442021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcomp.2021.757277/fullhttps://doaj.org/toc/2624-9898As depth cameras have gotten smaller, more affordable, and more precise, they have also emerged as a promising sensor in ubiquitous systems, particularly for detecting objects, scenes, and persons. This article sets out to systematically evaluate how suitable depth data can be for picking up users’ respiration, from small distance changes across the torso over time. We contribute a large public dataset of depth data over time from 19 persons taken in a large variety of circumstances. On this data, we evaluate and compare different state-of-the-art methods and show that their individual performance significantly depends on a range of conditions and parameters. We investigate the influence of the observed torso region (e.g., the chest), the user posture and activity, the distance to the depth camera, the respiratory rate, the gender, and user specific peculiarities. Best results hereby are obtained from the chest whereas the abdomen is least suited for detecting the user’s breathing. In terms of accuracy and signal quality, the largest differences are observed on different user postures and activities. All methods can maintain a mean accuracy of above 92% when users are sitting, but half of the observed methods only achieve a mean accuracy of 51% while standing. When users are standing and additionally move their arms in front of their upper body, mean accuracy values between the worst and best performing methods range from 21 to 87%. Increasing the distance to the depth camera furthermore results in lower signal quality and decreased accuracy on all methods. Optimal results can be obtained at distances of 1–2 m. Different users have been found to deliver varying qualities of breathing signals. Causes range from clothing, over long hair, to movement. Other parameters have shown to play a minor role in the detection of users’ breathing.Jochen KempfleKristof Van LaerhovenFrontiers Media S.A.articlerespiration sensingdepth imagingrgb-ddepth-based breathing estimationremote respiration measurementElectronic computers. Computer scienceQA75.5-76.95ENFrontiers in Computer Science, Vol 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	respiration sensing depth imaging rgb-d depth-based breathing estimation remote respiration measurement Electronic computers. Computer science QA75.5-76.95
spellingShingle	respiration sensing depth imaging rgb-d depth-based breathing estimation remote respiration measurement Electronic computers. Computer science QA75.5-76.95 Jochen Kempfle Kristof Van Laerhoven Breathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods
description	As depth cameras have gotten smaller, more affordable, and more precise, they have also emerged as a promising sensor in ubiquitous systems, particularly for detecting objects, scenes, and persons. This article sets out to systematically evaluate how suitable depth data can be for picking up users’ respiration, from small distance changes across the torso over time. We contribute a large public dataset of depth data over time from 19 persons taken in a large variety of circumstances. On this data, we evaluate and compare different state-of-the-art methods and show that their individual performance significantly depends on a range of conditions and parameters. We investigate the influence of the observed torso region (e.g., the chest), the user posture and activity, the distance to the depth camera, the respiratory rate, the gender, and user specific peculiarities. Best results hereby are obtained from the chest whereas the abdomen is least suited for detecting the user’s breathing. In terms of accuracy and signal quality, the largest differences are observed on different user postures and activities. All methods can maintain a mean accuracy of above 92% when users are sitting, but half of the observed methods only achieve a mean accuracy of 51% while standing. When users are standing and additionally move their arms in front of their upper body, mean accuracy values between the worst and best performing methods range from 21 to 87%. Increasing the distance to the depth camera furthermore results in lower signal quality and decreased accuracy on all methods. Optimal results can be obtained at distances of 1–2 m. Different users have been found to deliver varying qualities of breathing signals. Causes range from clothing, over long hair, to movement. Other parameters have shown to play a minor role in the detection of users’ breathing.
format	article
author	Jochen Kempfle Kristof Van Laerhoven
author_facet	Jochen Kempfle Kristof Van Laerhoven
author_sort	Jochen Kempfle
title	Breathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods
title_short	Breathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods
title_full	Breathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods
title_fullStr	Breathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods
title_full_unstemmed	Breathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods
title_sort	breathing in-depth: a parametrization study on rgb-d respiration extraction methods
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/76d6100f042d49ea986636e49f137044
work_keys_str_mv	AT jochenkempfle breathingindepthaparametrizationstudyonrgbdrespirationextractionmethods AT kristofvanlaerhoven breathingindepthaparametrizationstudyonrgbdrespirationextractionmethods
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Breathing In-Depth: A Parametrization Study on RGB-D Respiration Extraction Methods

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