Dissertation/Thesis Abstract

Video-Based Gait Analysis for Health Monitoring
by Liu, Xinyi, Ph.D., University of Arkansas at Little Rock, 2020, 63; 28087879
Abstract (Summary)

Human gait is essential for long-term health monitoring as it reflects physical and neurological aspects of a person's health status. In this thesis, we proposed a non-invasive video-based gait analysis system to detect abnormal gait, and record gait and postural parameters on a day-to-day basis. The system does not require subjects to wear any sensors or attach markers on their body, therefore it is convenient to use in daily life. It takes videos captured from a single camera as input. OpenPose is used to localize skeleton and joints in each frame. Angles of body parts form multivariate time series. Then we employed time series analysis for normal and abnormal gait classification. There are two methods implemented and compared in this thesis: BOSSVS (Bag-of-SFA-Symbols in Vector Space) based and DTW (Dynamic-Time Wrapping) - SVM (Support Vector Machine) based. They classify normal and abnormal gait by characterizing subjects' gait pattern and measuring deviation from their normal gait. In the experiment, we captured videos of our volunteers showing normal gait as well as simulated abnormal gait to validate the proposed methods. Both methods show promising results in intra-subject and inter-subject cross validation. From the gait and postural parameters, we observed distinction between normal and abnormal gait group. It shows that by recording and tracking these parameters, we can quantitively analyze how gait has changed over time.

Indexing (document details)
Advisor: Milanova, Mariofanna
Commitee: Iqbal, Kamran, Berleant, Daniel, Springer, Jan P., O’Gorman, Lawrence
School: University of Arkansas at Little Rock
Department: Systems Engineering
School Location: United States -- Arkansas
Source: DAI-B 82/3(E), Dissertation Abstracts International
Subjects: Computer science, Biomechanics
Keywords: Human gait analysis, Health monitoring, Non-invasive video-based gait analysis system
Publication Number: 28087879
ISBN: 9798672151557
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