Respiration Rate Estimation from Remote PPG via Camera in Presence of Non-Voluntary Artifacts
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 persons 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 quality of respiration signal and remove artifacts from the remote PPG signal, which results in reducing the MAE from 4.5bpm to 2.8bpm for RR in range of 5-25bpm. We validate the accuracy of our methodology using smartphone video recordings of 30 subjects with uniform distribution of skin tone.
Author: Korosh Vatanparvar, Migyeong Gwak, Li Zhu, Jilong Kuang, Alex Gao
Published: IEEE-EMBS International Conference on Biomedical and Health Informatics(BHI) and the Body Sensor Networks(BSN) Conferences (IEEE BHI & BSN)
Date: Sep 27, 2022