Biosignal Extraction and Analysis from Remote Video: Towards Real-world Implementation and Diagnosis Support
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
IT116, Linnanmaa
Topic of the dissertation
Biosignal Extraction and Analysis from Remote Video: Towards Real-world Implementation and Diagnosis Support
Doctoral candidate
Master of Science Constantino Álvarez Casado
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Center for Machine Vision and Signal Analysis (CMVS)
Subject of study
Computer Science and Engineering
Opponent
Docent Jorma Laaksonen, Aalto University
Custos
Associate professor Miguel Bordallo López, University of Oulu
Remote health monitoring: assistive diagnosis support through video analysis
Healthcare today is grappling with big issues like caring for more elderly people, a shortage of medical staff, and the challenges of providing care in remote and less populated areas. However, there is promising news. Breakthroughs in smart video technology, driven by computer vision and artificial intelligence, are opening up new possibilities to support healthcare professionals.
This research focuses on how we can harness video technology to detect vital health signs in a patient-friendly, non-intrusive way. We are working to improve the reliability and accuracy of two key techniques, remote photoplethysmography (rPPG) and remote ballistography (rBSG). These methods use camera feeds and video connections to help doctors identify signs of stress, depression, and respiratory illnesses, all without physical contact with the patient.
A major aim is to integrate these advanced tools seamlessly into real healthcare environments, taking into account the constraints of network and computing resources. This thesis delves into these challenges, seeking practical ways to make these technologies work effectively.
The ultimate goal is clear: to enhance healthcare accessibility and quality for all. By advancing how we diagnose and monitor health conditions through video technology, we aim to provide doctors with powerful, contactless tools that complement traditional methods and enrich the information available to them.
This research focuses on how we can harness video technology to detect vital health signs in a patient-friendly, non-intrusive way. We are working to improve the reliability and accuracy of two key techniques, remote photoplethysmography (rPPG) and remote ballistography (rBSG). These methods use camera feeds and video connections to help doctors identify signs of stress, depression, and respiratory illnesses, all without physical contact with the patient.
A major aim is to integrate these advanced tools seamlessly into real healthcare environments, taking into account the constraints of network and computing resources. This thesis delves into these challenges, seeking practical ways to make these technologies work effectively.
The ultimate goal is clear: to enhance healthcare accessibility and quality for all. By advancing how we diagnose and monitor health conditions through video technology, we aim to provide doctors with powerful, contactless tools that complement traditional methods and enrich the information available to them.
Last updated: 23.1.2024