Respiratory Pattern Recognition from Low-Resolution Thermal Imaging

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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 languageEnglish
Title of host publicationProceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020
Pages469-474
Number of pages6
ISBN (Electronic)9782875870742
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Virtual, Bruges, Belgium
Duration: 2 Oct 20204 Oct 2020
Conference number: 28

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN
CountryBelgium
CityBruges
Period02/10/202004/10/2020
OtherVirtual

Keywords

  • Thermal camera
  • Respiratory monitoring

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