Dedicated Exposure Control for Remote Photoplethysmography

Jordan Laurie,Niall Higgins,Thierry Peynot,Jonathan Roberts

doi:10.1109/access.2020.3003548

Jordan Laurie, Niall Higgins + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3003548

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Abstract

This paper aims to show that control of exposure time during video capture will improve the accuracy of remote photoplethysmography (rPPG). We propose a purpose specific exposure control algorithm for use in heart rate estimation via rPPG applicable for any controllable camera. Our novel algorithm works by selecting exposure that acheives maximum Signal-to-Noise Ratio (SNR) before distortion will occur. We performed experiments to test the accuracy of non-contact PPG extracted simultaneously from two identical cameras positioned together but with different exposure time controls. Our purpose specific algorithm in camera A controlled exposure time to maximise rPPG SNR ratio while camera B remained set at one of a range of values. Exposure time set by our novel algorithm out-performed camera B with a lower mean absolute error relative to a standard pulse oximeter. A significant improvement to heart rate estimation performance using a research camera can be made with specific control of exposure time. The improvements in performance demonstrated here are an important step in taking rPPG out of a lab environment and into less controlled circumstances such clinical settings and emergency rescue scenarios.

Highlights

Vital signs assessment using cameras, known as remote photoplethysmography, has shown significant promise in recent years [6], [8], [11]
We propose a method for exposure control that is purpose specific to heart rate (HR) assessment through remote photoplethysmography (rPPG)
Trends in MAE and 6BPM time over all participants are shown in Figures 11 and 12

Summary

Introduction

Vital signs assessment using cameras, known as remote photoplethysmography (rPPG), has shown significant promise in recent years [6], [8], [11]. There has been little reporting on how performance changes or degrades as conditions become less desirable This difficulty in less controlled environments is a key limitation in the clinical usage of rPPG. The most accepted method for rPPG extraction employs normal room lighting and standard RGB cameras [26] This approach of using ambient lighting conditions continues to be used, in part due to it being readily accessible [6], [8], [11]. 2) SELECT ROI TO CREATE TIME SERIES Recent developments have automated ROI selection so that this function is integrated with the rest of the HR extraction procedure [1], [6], [8], [11] These studies initially employed the Voila-Jones (VJ) face identification algorithm. A more efficient approach has been to apply the KanadeLucas-Tomasi (KLT) feature tracker to subsequent frames once the face is identified [24]

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