Camera-based respiratory monitoring is contactless, non-invasive, unobtrusive, and easily accessible compared to conventional wearable devices. Therefore, this paper presents a novel multimodal approach to estimating breathing rate based on tracking the movement and color changes of the face through an RGB camera. A machine learning model determines the final breathing rate between two separately calculated ones from breathing motion and remote photophysiography (rPPG). Our proposed pipeline is evaluated with 140 facial video recordings from 22 healthy subjects, including 6 controlled and 2 spontaneous breathing tasks ranging from 5 to 30 BPM. The estimation accuracy achieves 1.33 BPM mean absolute error and 86.53% pass rate within 2 BPM error criteria. To the best of our knowledge, our approach outperforms previous works that use a face region alone with a single RGB camera.

https://ieeexplore.ieee.org/document/10446086