Physiological measurements based on remote photoplethysmography
Keys words:Computer vision, signal processing, biomedical engineering, remote photoplethysmography (rPPG)
The work will take place at Dijon (Univ. Bourgogne) in collaboration with CNES https://goo.gl/mpM3AC
Photoplethysmography (PPG) is a non-invasive technique for detecting microvascular blood volume changes in tissues. This project aims to investigate on remote photoplethysmography (rPPG) for monitoring of human’s physiological parameters without any contact.
With the emergence of camera-based health care monitoring, remote photoplethysmography (rPPG) has recently been developed  as it allows remote physiological measurements only based on the ambient light and a video camera, hence reducing user constraint and without any expensive and specialist hardware requirement. Based on this seminal work, some research teams have proposed signal processing technics to improve the robustness of the original method. The rPPG technics are not yet as accurate as the ECG’s measurements nevertheless it seems obvious that, in a close future, they will enable a flexible monitoring of human vital signs (e.g. the heart rate, breathing rate) with a significant decreasing of user’s constraints or eventually extending the duration of the monitoring.
We have recently proposed in , a new remote photoplethysmography method mainly based on the selection of fine grain region of interests where rPPG is estimated. This work enables a large scope of perspectives to be investigated. Indeed, the rPPG signal, measured by a camera, precisely characterizes the cardio-vascular system. Each cardiac cycle appears as a peak in the temporal measured signal. The shape and the temporal variations of the signal are modified according to several physiological features.
The main objective of this PhD thesis is to explore and investigate the potential of the remote photoplethysmography to extract new physiological parameters without contact. For instance, the following features will be considered: the elasticity of arteries, the Perfusion Index (PI), the Pleth Variability Index (PVI, the Heart Rate Variability (HRV) or the Pulse Transit Time (PTT).
The PhD candidate will be tightly integrated in a research team specialized in the development of innovative image processing algorithms and the implementation of hardware and software systems with high temporal constraints. The candidate is expected to have a Master or Engineer degree in a relevant subject area (Image Processing, Applied Mathematics, Biomedical engineering or Computer Science). Strong analytical/mathematical skills are essential. Good programming experience is preferable. It is expected that the candidate has good communication skills, especially in written English. The candidate must be committed to deliver excellence in research!
To apply, please email:
 W. Verkruysse, L. O. Svaasand, J. S. Nelson, Remote plethysmographic imaging using ambient light, Optics express 16 (26) (2008) 21434-21445.
 S. Bobbia, Y. Benezeth, J. Dubois, Remote Photoplethysmography Based on Implicit Living Skin Tissue Segmentation, Best Paper T
(c) GdR 720 ISIS - CNRS - 2011-2015.