Imaging Photoplethysmography (iPPG) is an emerging optical imaging technology to non-invasively detect changes of microvascular blood volume in tissues with selective region of interest. Loughborough University iPPG is one of the pioneering outcomes derived from fundamental research of opto-physiological interaction (NASA/TM-2011-216145) employing a Lambert-Beer law and motion artefact photoplethysmography (PPG). However, the present state of introduction of iPPG into imaging monitoring and diagnosis systems is still far away from meeting criteria of clinical setups for routine monitoring and assessment, and individual health monitoring due to uncontrollable motion artefacts. The goal of the PhD project is to explore an auto-correlation model of 3D moving objective(s) to establish a calibration-free iPPG for real-time vital-sign monitoring and blood perfusion mapping. This PhD project will integrate dynamic opto-physiological modelling into an enabling optoelectronic multi-image sensing architecture design of a user prototype leading to a motion-free imaging technology as follows: 1) Optimal opto-illumination of moving objective(s) with a well understanding of mechanical principles of blood-flow microcirculation. 2) A mathematical model to interpret a coordination of multiple cameras to auto-tract moving objective(s). 3) A deep learning (DL) architecture to enable image processing with a higher self-calibration and for accurate measurement. 4) A diverse and coherent engineering solution to provide motion resistance and real-time functionalities of calibration-free iPPG with a better performance of physiological measurements. 5) Physiological testing to be undertaken not only to validate a 3D auto-track model for iPPG applications but also to gain a further real-time insight into physiological variations.
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Physical sciences (Applied Physics) or Engineering (Electronic Engineering, Computer Programming).
A relevant Master's degree and / or experience in one or more of the following will be an advantage: Embedded Software, Electronics, Signal processing, opto-physiological monitoring.