Date of Award
Spring 4-7-2025
Degree Type
Thesis
Degree Name
Master of Science (MS)
School
School of Computing
First Advisor
Casey Bennett, PhD
Second Advisor
Jacob Furst, PhD
Third Advisor
David Ramsay, PhD
Abstract
This thesis investigates trade-offs between signal quality and data coverage in photoplethysmographic (PPG) heart rate variability (HRV) monitoring using wrist-worn devices. The goal was to evaluate whether wrist placement and signal processing techniques can improve measurement reliability in real-world conditions. Data was collected from healthy participants wearing smartwatches on both wrists during rest and a structured math task introducing natural wrist movement. Three distinct processing methodologies were compared, including a proposed Rolling-Standardized Derivative (RSD) approach. Results showed that while HRV signals from both wrists were highly correlated at rest, motion caused a measurable drop in signal quality and inter-wrist agreement, especially on the dominant side. However, the RSD method significantly outperformed traditional algorithms in preserving usable data and maintaining correlation under motion. These findings suggest that improved signal processing can help offset motion artifacts and support more flexible sensor placement, enabling more ecologically valid HRV monitoring in real-world use cases.
Recommended Citation
Murphy, Andrew, "Evaluating wrist placement and signal processing techniques for real-world HRV monitoring using PPG" (2025). College of Computing and Digital Media Dissertations. 68.
https://via.library.depaul.edu/cdm_etd/68
Included in
Artificial Intelligence and Robotics Commons, Circulatory and Respiratory Physiology Commons