Markerless clinical gait identification using computer vision techniques and artificial intelligence
Date of Award
2025-08-01
Degree Name
Doctor of Philosophy
Department
Interdisciplinary Health Sciences
Advisor(s)
Jeffrey D. Eggleston
Abstract
Pose estimation algorithms could provide an unbiased examination for diagnosis, progression, and rehabilitation. PURPOSE: The purpose of this study was 1) to critically identify and appraise literature investigating spatiotemporal and kinematic gait characteristics in individuals with idiopathic Parkinson's Disease and spastic diplegic Cerebral Palsy compared to healthy controls; 2) to examine the validity and reliability of spatiotemporal gait characteristics and upper- and lower-body joint kinematics of OpenPose and the agreement with marker-based motion capture in healthy individuals; and 3) to examine the sensitivity, specificity and predictive values of a proposed clinical diagnostic tool using OpenPose in the diagnosis of idiopathic Parkinson's disease, spastic diplegic Cerebral Palsy and non-pathological gait. METHODS: First, meta-analysis was performed using the random-effects model for variables with at least three studies. Next, data (motion capture and digital video) were retrieved from an available dataset (Kwolek et al., 2019) and spatiotemporal gait characteristics and joint angle kinematics were calculated. One-way repeated measure ANOVAs, mean absolute errors, and ICC values were performed and calculated. Finally, reference data was retrieved from available literature, and 143 input videos were input into an identification MATLAB script. Sensitivity, specificity, positive predictive and negative predictive values, and likelihood ratios were calculated. RESULTS: Individuals with idiopathic Parkinson's disease and spastic diplegic Cerebral Palsy exhibited gait deviations when compared to healthy individuals. OpenPose can estimate spatiotemporal gait characteristics and sagittal upper and lower extremity joint angles in healthy gait. OpenPose performed with 32.47% and 93.10% sensitivity for Parkinson's disease and Cerebral Palsy, respectively. CONCLUSION: OpenPose could possess the capability to be implemented in a proposed diagnostic tool for distinguishing gait disorder.
Language
en
Provenance
Received from ProQuest
Copyright Date
2025-08
File Size
144 p.
File Format
application/pdf
Rights Holder
Katelyn Conroy
Recommended Citation
Conroy, Katelyn, "Markerless clinical gait identification using computer vision techniques and artificial intelligence" (2025). Open Access Theses & Dissertations. 4351.
https://scholarworks.utep.edu/open_etd/4351