Respiratory Pattern Recognition from Low-Resolution Thermal Imaging

Salla Aario; Ajinkya Gorad; Miika Arvonen; Simo Särkkä

Respiratory Pattern Recognition from Low-Resolution Thermal Imaging

Salla Aario^*, Ajinkya Gorad, Miika Arvonen, Simo Särkkä

^*Corresponding author for this work

Kuopio University Hospital

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

Abstract

Remote monitoring of vital signs has a wide range of applications. In this paper we propose a method to identify respiratory patterns from low-resolution thermal video data using a nearest neighbor data association (NNDA) and nearest neighbor Kalman filter (NNKF) based algorithms along with multi-class support vector machine (SVM). The method in this work is evaluated against breathing belt data as a reference, collected from healthy volunteers. Correlation of the proposed method with airflow derived from the breathing belt was found to be 0.7. The SVM classifier is able to distinguish between the breathing patterns from derived airflow with 60\% accuracy.

Original language	English
Title of host publication	Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020
Publisher	i6doc.com
Pages	469-474
Number of pages	6
ISBN (Electronic)	9782875870742
Publication status	Published - 2020
MoE publication type	A4 Conference publication
Event	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Virtual, Bruges, Belgium Duration: 2 Oct 2020 → 4 Oct 2020 Conference number: 28

Conference

Conference	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated title	ESANN
Country/Territory	Belgium
City	Bruges
Period	02/10/2020 → 04/10/2020

Keywords

Thermal camera
Respiratory monitoring

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https://www.esann.org/sites/default/files/proceedings/2020/ES2020-182.pdf

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title = "Respiratory Pattern Recognition from Low-Resolution Thermal Imaging",

abstract = "Remote monitoring of vital signs has a wide range of applications. In this paper we propose a method to identify respiratory patterns from low-resolution thermal video data using a nearest neighbor data association (NNDA) and nearest neighbor Kalman filter (NNKF) based algorithms along with multi-class support vector machine (SVM). The method in this work is evaluated against breathing belt data as a reference, collected from healthy volunteers. Correlation of the proposed method with airflow derived from the breathing belt was found to be 0.7. The SVM classifier is able to distinguish between the breathing patterns from derived airflow with 60\% accuracy.",

keywords = "Thermal camera, Respiratory monitoring",

author = "Salla Aario and Ajinkya Gorad and Miika Arvonen and Simo S{\"a}rkk{\"a}",

year = "2020",

language = "English",

pages = "469--474",

booktitle = "Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020",

publisher = "i6doc.com",

address = "Belgium",

note = "European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN ; Conference date: 02-10-2020 Through 04-10-2020",

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Aario, S, Gorad, A, Arvonen, M & Särkkä, S 2020, Respiratory Pattern Recognition from Low-Resolution Thermal Imaging. in Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020. i6doc.com, pp. 469-474, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 02/10/2020. <https://www.esann.org/sites/default/files/proceedings/2020/ES2020-182.pdf>

Respiratory Pattern Recognition from Low-Resolution Thermal Imaging. / Aario, Salla; Gorad, Ajinkya; Arvonen, Miika et al.
Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020. i6doc.com, 2020. p. 469-474.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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T1 - Respiratory Pattern Recognition from Low-Resolution Thermal Imaging

AU - Aario, Salla

AU - Gorad, Ajinkya

AU - Arvonen, Miika

AU - Särkkä, Simo

N1 - Conference code: 28

PY - 2020

Y1 - 2020

N2 - Remote monitoring of vital signs has a wide range of applications. In this paper we propose a method to identify respiratory patterns from low-resolution thermal video data using a nearest neighbor data association (NNDA) and nearest neighbor Kalman filter (NNKF) based algorithms along with multi-class support vector machine (SVM). The method in this work is evaluated against breathing belt data as a reference, collected from healthy volunteers. Correlation of the proposed method with airflow derived from the breathing belt was found to be 0.7. The SVM classifier is able to distinguish between the breathing patterns from derived airflow with 60\% accuracy.

AB - Remote monitoring of vital signs has a wide range of applications. In this paper we propose a method to identify respiratory patterns from low-resolution thermal video data using a nearest neighbor data association (NNDA) and nearest neighbor Kalman filter (NNKF) based algorithms along with multi-class support vector machine (SVM). The method in this work is evaluated against breathing belt data as a reference, collected from healthy volunteers. Correlation of the proposed method with airflow derived from the breathing belt was found to be 0.7. The SVM classifier is able to distinguish between the breathing patterns from derived airflow with 60\% accuracy.

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KW - Respiratory monitoring

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EP - 474

BT - Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

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T2 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 2 October 2020 through 4 October 2020

ER -

Respiratory Pattern Recognition from Low-Resolution Thermal Imaging

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Conference

Keywords

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