Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review
Journal
Data-Driven Innovation for Intelligent Technology : Perspectives and Applications in ICT
ISSN
2197-6503
Publisher
Springer
Date Issued
2024-01-01
Author(s)
Escobedo-Gordillo, Andrés
Type
Resource Types::text::book::book part
Abstract
Nowadays, the healthcare is a priority for both governments and persons. Vital sign monitoring allows knowing the health status and is widely used for prevention, diagnosis, and treatment of determined illnesses. In particular, breathing and heart rate are traditionally considered the most relevant and accessible vital signs. However, oxygen saturation was essential in the COVID-19 pandemic. On the other hand, contact techniques to estimate these vital signs are a standard monitoring reference. However, non-contact estimation methods have gained relevance in the last few years in those cases where there is the possibility of suffering stress, pain, and skin irritation in specific situations, as in the case of vulnerable skin in burn patients and neonates. In this chapter, a review of contactless video-based vital-sign methods is presented. The selected methods have a data-driven approach as an alternative when there is not theoretical model of the physiological phenomenon. Finally, a new framework with a general data-driven approach to estimate the most used vital signs is proposed. This framework includes a region of interest extraction stage, a video magnification technique to reveals subtle changes, and a machine learning method to estimate the vital signs. In addition, each step describes some recommendations and best practices found ©Springer.
License
Acceso Restringido
How to cite
Brieva, J., Moya-Albor, E., Ponce, H., Escobedo-Gordillo, A. (2024). Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review. In: Ponce, H., Brieva, J., Lozada-Flores, O., Martínez-Villaseñor, L., Moya-Albor, E. (eds) Data-Driven Innovation for Intelligent Technology. Studies in Big Data, vol 148. Springer, Cham. https://doi.org/10.1007/978-3-031-54277-0_1