RGB Camera-based Physiological Sensing: Challenges and Future Directions

TL;DR

This paper reviews the challenges and future directions of RGB camera-based physiological sensing, emphasizing its potential in healthcare and the need to address key research hurdles for practical deployment.

Contribution

It identifies four major research challenges in RGB camera-based physiological sensing and proposes future directions to overcome these, advancing AI-driven healthcare applications.

Findings

Four key research challenges identified

Future directions proposed for each challenge

Framework for practical AI healthcare systems

Abstract

Numerous real-world applications have been driven by the recent algorithmic advancement of artificial intelligence (AI). Healthcare is no exception and AI technologies have great potential to revolutionize the industry. Non-contact camera-based physiological sensing, including remote photoplethysmography (rPPG), is a set of imaging methods that leverages ordinary RGB cameras (e.g., webcam or smartphone camera) to capture subtle changes in electromagnetic radiation (e.g., light) reflected by the body caused by physiological processes. RGB camera-based systems not only have the ability to measure the signals without contact with the body but also have the opportunity to capture multimodal information (e.g., facial expressions, activities and other context) from the same sensor. However, developing accessible, equitable and useful camera-based physiological sensing systems comes with various challenges. In this article, we identify four research challenges for the field of RGB camera-based physiological sensing and broader AI driven healthcare communities and suggest future directions to tackle these. We believe solving these challenges will help deliver accurate, equitable and generalizable AI systems for healthcare that are practical in real-world and clinical contexts.