Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features With Transformers and Recursive Convolutional Models

Alicja Kwaśniewska; Maciej Szankin; Jacek Rumiński; Anthony Sarah; David Gamba

Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features With Transformers and Recursive Convolutional Models

Abstrakt

Non-contact evaluation of vital signs has been becoming increasingly important, especially in light of the COVID- 19 pandemic, which is causing the whole world to examine people’s interactions in public places at a scale never seen before. However, evaluating one’s vital signs can be a relatively complex procedure, which requires both time and physical contact between examiner and examinee. These re- quirements limit the number of people who can be efficiently checked, either due to the medical station throughput, pa- tients’ remote locations or the need for social distancing. This study is a first step to increasing the accuracy of com- puter vision-based respiratory rate estimation by transfer- ring texture information from images acquired in different domains. Experiments conducted with two deep neural net- work topologies, a recursive convolutional model and trans- formers, proved their robustness in the analyzed scenario by reducing estimation error by 50% compared to low resolu- tion sequences. All resources used in this research, including links to the dataset and code, have been made publicly available.

Autorzy (5)

Alicja Kwaśniewska mgr inż
Maciej Szankin mgr inż.
Jacek Rumiński prof. dr hab. inż.
Anthony Sarah
David Gamba

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Wersja publikacji: Accepted albo Published Version
Licencja: Copyright (2021 Authors)

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Kategoria:: Aktywność konferencyjna
Typ:: materiały konferencyjne indeksowane w Web of Science
Język:: angielski
Rok wydania:: 2021
Opis bibliograficzny:: Kwaśniewska A., Szankin M., Rumiński J., Sarah A., Gamba D..: Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features With Transformers and Recursive Convolutional Models, W: , 2021, ,.
Weryfikacja:: Politechnika Gdańska