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.
Authors
- mgr inż Alicja Kwaśniewska,
- mgr inż. Maciej Szankin,
- prof. dr hab. inż. Jacek Rumiński link open in new tab ,
- Anthony Sarah,
- David Gamba
Additional information
- Category
- Aktywność konferencyjna
- Type
- materiały konferencyjne indeksowane w Web of Science
- Language
- angielski
- Publication year
- 2021
