Paper Info
Title
Comparison of Region of Interest Segmentation Methods for Video-Based Heart Rate Measurements
Abstract
Conventional contact photoplethysmography (PPG) sensors are not suitable in situations of skin damage or when unconstrained movement is required. As a consequence, remote photoplethysmography (rPPG) has recently emerged because it provides remote physiological measurements without expensive hardware and improves comfort for long term monitoring. RPPG estimation methods use the spatially averaged RGB values of pixels in a Region Of Interest (ROI) to generate a temporal RGB signal. The selection of ROI is a critical first step to obtain reliable pulse signals and must contain as many skin pixels as possible with a low percentage of non-skin pixels. In this paper, we experimentally compare seven ROI segmentation methods in the perspective of heart rate (HR) measurements with dedicated metrics. The algorithms are compared using our in-house database UBFC-RPPG, comprising of 53 videos specifically geared towards rPPG analysis.
Year
DOI
Venue
2018
10.1109/BIBE.2018.00034
2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)
Keywords
Field
DocType
remote photoplethysmography (rPPG),Heart Rate (HR),Region of Interest (ROI)
Computer vision,Computer science,Segmentation,Photoplethysmogram,RGB color model,Artificial intelligence,Pixel,Region of interest,Machine learning
Conference
ISSN
ISBN
Citations 
2159-5410
978-1-5386-5043-1
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Peixi Li100.34
Yannick Benezeth239926.11
Keisuke Nakamura3214.99
Randy Gomez47628.11
Chao Li5525110.37
Fan Yang6115.91