Motion-Resistant Remote Imaging Photoplethysmography Based on the Optical Properties of Skin

Remote imaging photoplethysmography (RIPPG) can achieve contactless monitoring of human vital signs. However, the robustness to a subject's motion is a challenging problem for RIPPG, especially in facial video-based RIPPG. The RIPPG signal originates from the radiant intensity variation of huma...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on circuits and systems for video technology Vol. 25; no. 5; pp. 879 - 891
Main Authors Litong Feng, Lai-Man Po, Xuyuan Xu, Yuming Li, Ruiyi Ma
Format Journal Article
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Blood volume pulse (BVP)
Face
Image color analysis
Lighting
motion artifact
Optical imaging
photoplethysmography (PPG)
radiometry
Reflection
remote imaging
Skin
skin optics
Tracking
photoplethysmography (PPG)
remote imaging
skin optics
motion artifact
Blood volume pulse (BVP)
radiometry
Online AccessGet full text

Cover

More Information
Summary:Remote imaging photoplethysmography (RIPPG) can achieve contactless monitoring of human vital signs. However, the robustness to a subject's motion is a challenging problem for RIPPG, especially in facial video-based RIPPG. The RIPPG signal originates from the radiant intensity variation of human skin with pulses of blood and motions can modulate the radiant intensity of the skin. Based on the optical properties of human skin, we build an optical RIPPG signal model in which the origins of the RIPPG signal and motion artifacts can be clearly described. The region of interest (ROI) of the skin is regarded as a Lambertian radiator and the effect of ROI tracking is analyzed from the perspective of radiometry. By considering a digital color camera as a simple spectrometer, we propose an adaptive color difference operation between the green and red channels to reduce motion artifacts. Based on the spectral characteristics of photoplethysmography signals, we propose an adaptive bandpass filter to remove residual motion artifacts of RIPPG. We also combine ROI selection on the subject's cheeks with speeded-up robust features points tracking to improve the RIPPG signal quality. Experimental results show that the proposed RIPPG can obtain greatly improved performance in accessing heart rates in moving subjects, compared with the state-of-the-art facial video-based RIPPG methods.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2014.2364415