Multimodal Breathing Rate Estimation Using Facial Motion and RPPG From RGB Camera

Camera-based respiratory monitoring is contactless, non-invasive, unobtrusive, and easily accessible compared to conventional wearable devices. 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 cam...

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Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 2046 - 2050
Main Authors Gwak, Migyeong, Vatanparvar, Korosh, Zhu, Li, Rashid, Nafiul, Ahmed, Mohsin, Bae, Jungmok, Kuang, Jilong, Gao, Alex
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.04.2024
Subjects
breathing motion tracking
Breathing rate
camera
Cameras
Estimation
Faces
Monitoring
multimodal
remote PPG
Tracking
Wearable devices
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Summary:Camera-based respiratory monitoring is contactless, non-invasive, unobtrusive, and easily accessible compared to conventional wearable devices. 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 photoplethysmography (rPPG) to improve the measurement performance in a broader range of breathing frequencies. 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.
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10446086