Graduation Semester and Year
2017
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Vassilis Athitsos
Abstract
Human gait has shown to be a strong indicator of health issues under a wide variety of conditions. For that reason, gait analysis has become a powerful tool for clinicians to assess functional limitations due to neurological or orthopedic conditions that are reflected in gait. Therefore, accurate gait monitoring and analysis methods have found a wide range of applications from diagnosis to treatment and rehabilitation. This thesis focuses on creating a low-cost and non-intrusive vision-based machine learning framework dubbed as iGait to accurately detect CLBP patients using 3-D capturing devices such as MS Kinect. To analyze the performance of the system, a precursor analysis for creating a feature vector is performed by designing a highly controlled in-lab simulation of walks. Furthermore, the designed framework is extensively tested on real- world data acquired from volunteer elderly patients with CLBP. The feature vector presented in this thesis show very high agreement in getting the pathological gait disorders (98% for in-lab settings and 90% for actual CLBP patients), with a thorough research on the contribution of each feature vector on the overall classification accuracy.
Keywords
CLBP, Kinect
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Sayed, Saif, "iGait: Vision-based Low-Cost, Reliable Machine Learning Framework for Gait Abnormality Detection" (2017). Computer Science and Engineering Theses. 321.
https://mavmatrix.uta.edu/cse_theses/321
Comments
Degree granted by The University of Texas at Arlington