Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/45956
Title: Markerless Upper Body Movement Tracking During Gait in Children with HIV Encephalopathy: A Pilot Study
Authors: Eken, Maaike M.
MEYNS, Pieter 
Lamberts, Robert P.
Langerak, Nelleke G.
Issue Date: 2025
Publisher: MDPI
Source: Applied Sciences, 15 (8) (Art N° 4546)
Abstract: The aim of this pilot study was to investigate the feasibility of markerless tracking to assess upper body movements of children with and without human immunodeficiency virus encephalopathy (HIV-E). Sagittal and frontal video recordings were used to track anatomical landmarks with the DeepLabCut pre-trained human model in five children with HIV-E and five typically developing (TD) children to calculate shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension and trunk lateral sway. Differences in joint angle trajectories of the two cohorts were investigated using a one-dimensional statistical parametric mapping method. Children with HIV-E showed a larger range of motion in shoulder abduction and trunk sway than TD children. In addition, they showed more shoulder extension and more lateral trunk sway compared to TD children. Markerless tracking was feasible for 2D movement analysis and sensitive to observe expected differences in upper limb and trunk sway movements between children with and without HIVE. Therefore, it could serve as a useful alternative in settings where expensive gait laboratory instruments are unavailable, for example, in clinical centers in low- to middle-income countries. Future research is needed to explore 3D markerless movement analysis systems and investigate the reliability and validity of these systems against the gold standard 3D marker-based systems that are currently used in clinical practice.
Notes: Meyns, P (corresponding author), Hasselt Univ, Fac Rehabil Sci, REVAL Rehabil Res Ctr, B-3500 Hasselt, Belgium.
meken@sun.ac.za; pieter.meyns@uhasselt.be; nellekelangerak@hotmail.com
Keywords: machine learning;deep learning;clinical gait analysis;arm swing;posture;trunk;DeepLabCut
Document URI: http://hdl.handle.net/1942/45956
e-ISSN: 2076-3417
DOI: 10.3390/app15084546
ISI #: 001474679300001
Rights: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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