Respiration Rate Estimation from Remote PPG via Camera in Presence of Non-Voluntary Artifacts
Korosh Vatanparvar, Migyeong Gwak, Li Zhu, Jilong Kuang, Alex Gao
Abstract
Contactless measurement of vitals has been seen as a promising alternative to contact sensors for monitoring of health condition. In this paper, we focus on respiration rate (RR) as one of the fundamental biomarkers of a person’s cardio and pulmonary activities. Remote RR estimation has gained attraction due to its various potential applications; use of RGB cameras to extract remote photoplethysmography (PPG) signal from subjects’ face has been debated as one of the enabling technologies for remote RR estimation. The technology is challenged with respect to wide range of RR and non-voluntary motion in uncontrolled settings. We propose a novel methodology to enhance the remote PPG signal and remove artifacts from the respiration signal. The method achieves 3.9bpm MAE of 90% percentile (1.3bpm decrease) for estimating RR in range of 5-25bpm. We validate the performance using smartphone video recordings of 30 subjects with uniform distribution of skin tone.