Deep Learning-Driven Innovations in PPG Sensing Technology
Date of Award
12-12-2024
Degree Name
M.S. in Computer Science
Department
Department of Computer Science
Advisor/Chair
Tianming Zhao
Abstract
Photoplethysmogram (PPG) signals are a highly versatile biological signal that can be extracted from a subject and used for several purposes including health monitoring and continuous authentication. Interest in PPG extraction techniques has been growing recently, especially in the field of remote PPG (rPPG), which seeks to extract PPG signals from a subject with no contact device. These rPPG models process the video in various ways to distill it to its most relevant information for cardiac signal extraction, but artifacts caused by subject motions and lighting conditions in the video remain a problem due to distortions in the waveform, making heart rate extraction difficult. In the first project of this thesis, we propose a noise-aware post-processor network that takes generated position, head pose, and luminance information from the video as noise-correlating signals to be used to denoise an rPPG signal generated by an existing base network. We show effective results on two separate datasets, PURE and MMPD, while using two different representative base models, DeepPhys and PhysNet, in conjunction with our post-processor, reducing mean absolute error by an average of 26\% across all tests. Additionally, we develop novel techniques for contact PPG sensor manipulation and control. The ability to control a physical PPG sensor has wide-ranging applications in the PPG space, from the testing of medical devices or continuous authentication systems to performing presentation attacks. In the second project of this thesis, we devise a system to teach a reinforcement learning agent how to manipulate a physical PPG sensor to match a target signal using Proximal Policy Optimization (PPO). We show strong results on three representative signals and discuss effective training and reward strategies to overcome the difficulties presented by each signal.
Keywords
deep learning, photoplethysmogram, PPG, reinforcement learning, noise-aware learning, remote PPG
Rights Statement
Copyright © 2024, author.
Recommended Citation
Holsinger, Adam Nicholas, "Deep Learning-Driven Innovations in PPG Sensing Technology" (2024). Graduate Theses and Dissertations. 7476.
https://ecommons.udayton.edu/graduate_theses/7476
