A systematic review of contactless respiratory rate measurement using RGB cameras

Objective. The advancement of contactless methods of measuring the respiratory rate (RR) using RGB cameras demonstrates a significant potential for improving patient care in various environments. As these methods offer reliable and discreet monitoring, they can prevent severe health complications and improve outcomes for patients facing challenges accessing traditional healthcare facilities.Approach. This systematic review explores recent advancements in RR estimation using RGB cameras, focusing on assessing publicly available datasets and effective signal preprocessing methods. We also conducted a comprehensive analysis by comparing RGB camera-based approaches with other sensor modalities and discussed potential future research directions and indicated the necessity of developing new approaches that would mitigate existing challenges and would enhance the accuracy and reliability of non-contact RR measurement methods.Main results. We analyzed existing public datasets, assessing their diversity in lighting, skin tone, and motion, alongside the camera hardware configurations, including frame rate and resolution, utilizing different filter and feature-based techniques. While deep learning and hybrid models achieved lower errors under ideal indoor lighting and minimal motion, performance significantly declined in low light, high motion, or complex uncontrolled environments. In contrast, other sensor modalities, such as thermal and infrared sensors, achieved high accuracy across a wide range of conditions, but at greater hardware cost and system complexity, while RGB cameras remained the most cost-effective option, trading off precision for accessibility.Significance. RGB camera-based RR monitoring systems have the potential for robust applicability in clinical and nonclinical settings such as telemedicine platforms for monitoring patients breathing rates (BRs) in real time. This review highlights existing research gaps, such as insufficient real-world datasets and sensitivity to environmental variance, and emphasizes on the importance of acquiring datasets based on complex real-world scenarios, standardized benchmarks, multi-sensor fusion for addressing current limitations, and deep neural network architecture implementation for reliable non-contact RR estimation for real-world applications.