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

^*Tämän työn vastaava kirjoittaja

Kuopio University Hospital

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

4 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020
Kustantaja	i6doc.com
Sivut	469-474
Sivumäärä	6
ISBN (elektroninen)	9782875870742
Tila	Julkaistu - 2020
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Virtual, Bruges, Belgia Kesto: 2 lokak. 2020 → 4 lokak. 2020 Konferenssinumero: 28

Conference

Conference	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Lyhennettä	ESANN
Maa/Alue	Belgia
Kaupunki	Bruges
Ajanjakso	02/10/2020 → 04/10/2020

Pääsy asiakirjaan

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

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

@inproceedings{3d2ef803308d4364854677366780d0a2,

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",

}

Aario, S , Gorad, A, Arvonen, M & Särkkä, S 2020, Respiratory Pattern Recognition from Low-Resolution Thermal Imaging. julkaisussa Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020. i6doc.com, Sivut 469-474, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgia, 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. s. 469-474.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

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.

KW - Thermal camera

KW - Respiratory monitoring

UR - http://www.scopus.com/inward/record.url?scp=85098992374&partnerID=8YFLogxK

M3 - Conference article in proceedings

SP - 469

EP - 474

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

PB - i6doc.com

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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