Award Date
12-1-2024
Degree Type
Thesis
Degree Name
Master of Science in Engineering (MSE)
Department
Electrical and Computer Engineering
First Committee Member
Sarah Harris
Second Committee Member
Brendan Morris
Third Committee Member
Shengjie Zhai
Fourth Committee Member
David Lee
Number of Pages
55
Abstract
We developed an algorithm that aims to move us closer to detecting early signs of arthritis. The program processes and analyzes X-ray videos using coyote and dog cadavers as models to examine the range of motion around the hip and connecting joints using optical flow techniques that track motion and velocity. We focus on how optical flow techniques track embedded metal markers and verify accuracy through comparisons with XMALab (X-ray motion analysis lab). Once proven as an accurate alternative, the focus will switch to markerless tracking and become a proof-of-concept for optical flow to be used in place of XMALab, which requires markers to detect the motion of the hip joint. In the future, this algorithm may be developed further into a predictive model using machine learning that would be able to detect arthritis in animals, including humans, before outward signs of arthritis arise. This method aims to enhance diagnosis accuracy and efficiency as well as allow experts in the field more time for intervention and prevention to improve the patient’s outcome regarding arthritis.
Keywords
Optical Flow
Disciplines
Computer Engineering | Electrical and Computer Engineering
File Format
File Size
1,649 KB
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Paperno, Isabella, "Tracking Joint Movement Using Optical Flow" (2024). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5197.
https://digitalscholarship.unlv.edu/thesesdissertations/5197
Rights
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