RGB Camera-based Physiological Sensing: Challenges and Future Directions
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
MobilePhys: Personalized Mobile Camera-Based Contactless Physiological Sensing
Camera-based contactless photoplethysmography refers to a set of popular techniques for contactless physiological measurement. The current state-of-the-art neural models are typically trained in a supervised manner using…
The 1st Challenge on Remote Physiological Signal Sensing (RePSS)
Remote measurement of physiological signals from videos is an emerging topic. The topic draws great interests, but the lack of publicly available benchmark databases and a fair validation platform are hindering its furth…
Synthetic Data for Multi-Parameter Camera-Based Physiological Sensing
Synthetic data is a powerful tool in training data hungry deep learning algorithms. However, to date, camera-based physiological sensing has not taken full advantage of these techniques. In this work, we leverage a high-…
Towards Mobile Sensing with Event Cameras on High-agility Resource-constrained Devices: A Survey
With the increasing complexity of mobile device applications, these devices are evolving toward high agility. This shift imposes new demands on mobile sensing, particularly in terms of achieving high accuracy and low lat…
3D ReconstructionEvent-based visionObject TrackingOptical Flow Estimation+2A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges and Opportunities
The threat posed by wildfires or bushfires has become a severe global issue due to the increase in human activities in forested areas and the impact of climate change. Consequently, there is a surge in the development of…
Anomaly Detection