Electronic Theses and Dissertations

Identifier

6776

Author

Bin Feng

Date

2021

Date of Award

12-21-2021

Document Type

Thesis

Degree Name

Master of Science

Major

Electrical and Computer Engr

Committee Chair

Ana Doblas

Committee Member

Aaron Robinson

Committee Member

Doug Powell

Committee Member

Madhusudhanan Balasubramanian

Abstract

Motion capture systems are widely used for measuring athletic performance and as a diagnostic tool in sports medicine. Standard motion capture systems record body movement using: (1) a set of cameras to localize body segments; or (2) specialized suits in which inertial measurement units are directly attached to body segments. Major drawbacks of these systems are limited portability, affordability, and accessibility. This contribution presents a markerless motion capture system using a commercially available sports camera and the OpenPose human pose estimation algorithm. We have validated the proposed markerless system by analyzing the human biometrics during running and jumping movements. The findings of this study demonstrate that pairing a low-cost sports camera with artificial intelligence allows for high-quality analysis of human movement.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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