Job Description Summary

Undergraduate student research assistant will assist with a project that focuses on developing a low-cost, non-invasive optical imaging system which measures subtle changes in facial blood flow associated with concussions and autonomic nervous system dysfunction. Using a standard digital camera, we will employ remote photoplethysmography (rPPG) to detect small variations in facial skin color caused by changes in blood volume and circulation.

Responsibilities

The undergraduate student research assistant will help develop Python-based image processing and signal analysis algorithms to track blood flow across different facial regions, extract physiological signals such as heart rate and low-frequency vascular oscillations, and examine how these signals respond to controlled physiological challenges. Techniques including facial landmark tracking, spatial mapping, Fourier analysis, and singular value decomposition will be used to identify patterns of vascular activity that may distinguish healthy individuals from patients recovering from concussions. The ultimate goal is to explore whether inexpensive, camera-based measurements can provide objective biomarkers to support concussion assessment and recovery monitoring.

Required Qualifications

Student should have an understanding of programming, have a strong background in mathematics especially linear algebra.