Year: 2025
Type: article
Abstract: Remote measurement of respiratory behaviour through RGB cameras has gained significant attention in the last couple of decades. Unlike traditional contact-based methods that may cause discomfort and require specialised equipment, contactless physiological measurement techniques offer a non-invasive way to monitor vital signs. In this survey article, we comprehensively review the literature and techniques related to estimating respiratory information from RGB cameras. We categorise the approaches into three main groups: methods utilising respiration-induced body movements, methods extracting respiratory information from blood volume pulse signals obtained via remote photoplethysmography, and deep learning-based techniques for direct respiratory signal extraction. To evaluate these approaches, we perform a comparative assessment using publicly available datasets. As a result, we uncover emerging trends while identifying strengths and weaknesses in the field. Our contributions include a detailed review of the literature, a benchmark of representative methods on multiple datasets, and the introduction of a new Python package called resPyre that implements the benchmarked approaches, making them accessible to the research community. This survey aims at promoting reproducibility, facilitate further research, and guide the development of more accurate and practical methods for remote respiration measurement using RGB cameras.
Source: ACM Computing Surveys
DOI: 10.1145/3771763
Institutions: University of Milan, University of Modena and Reggio Emilia
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415147672