Multimodal Breathing Rate Estimation Using Facial Motion and RPPG From RGB Camera
Migyeong Gwak, Korosh Vatanparvar, Li Zhu, Nafiul Rashid, Mohsin Y. Ahmed, Jungmok Bae, Jilong Kuang, Alex Gao
Abstract
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.
BibTeX
@inproceedings{icassp2024_multimodalbreath,
title = {Multimodal Breathing Rate Estimation Using Facial Motion and RPPG From RGB Camera},
author = {Migyeong Gwak and Korosh Vatanparvar and Li Zhu and Nafiul Rashid and Mohsin Y. Ahmed and Jungmok Bae and Jilong Kuang and Alex Gao},
booktitle = {ICASSP 2024},
year = {2024}
}