Graduate Research

Dissertation

Title: Exploration of the Photoplethysmography Signal and its Application to Wearable Devices
Successfully defended April 2023.

Graduate Research Interests

My graduate research interests revolved around wearable devices and robotics. Specifically, the use and dynamics behind photoplethysmography (PPG). PPG is a simple optical sensor that measures changes in blood volume levels commonly used to interpret heart rate and blood oxygen saturation. Throughout my graduate degree, I have focused on using embedded machine learning, dynamic discovery, and more to develop improved heart rate detection systems.

Three projects serve as the basis for my graduate research.

  • The first was a continuation of a graduate project on Embedded Heart Rate Estimation. Some major changes have been made to the project including the use of Edge Impulse and the addition of initial subject testing. This project focuses on Python, Arduino, Machine Learning, and wearable devices. A second algorithm using a sliding window and MATLAB was also implemented for comparison to the embedded ML method. (This work was presented at SKM, see below)
  • The second was a continuation of a graduate project on Data Driven Dynamic Discovery of the PPG. More work has been done exploring other nonlinear systems and fine-tuning the SINDy algorithm.
  • Laslty, was the project PBVI for Motion Artifact and Sensor System Energy Savings. A practical application for the project used an end-to-end embedded activity detection system with machine learning. Additionally, two filters were created to reduce and limit the effects of motion artifacts on PPG signals. (see publication below)

Publications

Sindorf J., and Redkar S. (2024). “PBVI for optimal photoplethysmography noise filter selection using human activity recognition observations for improved heart rate estimation on multi-sensor systems.” ASME Journal of Medical Devices.

Presentations

International Conference of Secure Knowledge Management (SKM) 2023. Find the slides here!

Sindorf J., and Redkar S. Embedded Machine Learning for Heart Rate Estimation with Arduino, International Conference on Secure Knowledge Management (SKM). Tempe, AZ. 2023.

Sindorf J., and Redkar S. Day-to-day Heart Rate Estimation with PPG for Embedded Sensor Systems, International Conference on Secure Knowledge Management (SKM). Tempe, AZ. 2023.