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

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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.

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Accepted albo Published Version
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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

wyświetlono 65 razy

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