Remote Patient Blood Glucose Monitoring using rPPG

The project will enable contactless measurement of blood glucose using remote Photoplethysmography (rPPG) technology using patients’ face or finger videos which is a comparatively new artefact. There is a lack of training data and the blood volume pulse (BVP) signal extracted from the videos suffer from a lot of noise resulting in poor measurement accuracy. The proposed research will develop novel signal extraction and noise reduction techniques, new machine learning models to measure blood glucose, and collect new training data to improve the reliability of the methods for remote video-based blood glucose monitoring. The technology can contribute to patient wellbeing by enabling non-invasive blood glucose measurement, improve the quality of remote healthcare provisioning by allowing physicians to remotely check patients’ blood glucose levels, improve wellbeing of the caregivers by simplifying the task, and prevent serious health problems and hospitalization due to improper blood glucose levels.

Faculty Supervisor:

Farhana Zulkernine

Student:

Partner:

MarkiTech

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

Queen's University

Program: