"Tracking Joint Movement Using Optical Flow" by Isabella Paperno

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

PDF

File Size

1,649 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/


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