Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy

The main purpose of this study is to explore the feasibility of deep learning based markerless motion capture in clinical settings. This study compares the markerless motion capture (Blazepose) to the gold standard marker-based system (Vicon) by assessing the hip, knee, and ankle joint flexion angle...

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Main Author: Mustafaoglu, Afet
Other Authors: Liikuntatieteellinen tiedekunta, Faculty of Sport and Health Sciences, Liikunta- ja terveystieteet, Sport and Health Sciences, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:eng
Published: 2023
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/92224
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author Mustafaoglu, Afet
author2 Liikuntatieteellinen tiedekunta Faculty of Sport and Health Sciences Liikunta- ja terveystieteet Sport and Health Sciences Jyväskylän yliopisto University of Jyväskylä
author_facet Mustafaoglu, Afet Liikuntatieteellinen tiedekunta Faculty of Sport and Health Sciences Liikunta- ja terveystieteet Sport and Health Sciences Jyväskylän yliopisto University of Jyväskylä Mustafaoglu, Afet Liikuntatieteellinen tiedekunta Faculty of Sport and Health Sciences Liikunta- ja terveystieteet Sport and Health Sciences Jyväskylän yliopisto University of Jyväskylä
author_sort Mustafaoglu, Afet
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description The main purpose of this study is to explore the feasibility of deep learning based markerless motion capture in clinical settings. This study compares the markerless motion capture (Blazepose) to the gold standard marker-based system (Vicon) by assessing the hip, knee, and ankle joint flexion angles of cerebral palsy (CP) patients and their typically developed (TD) peers. The participant group included six CP patients and six TD individuals. The participants walked on an 8-meter gait path at a self-selected pace. 11 Vicon cameras (200 Hz) and 3 GoPro (60 Hz) cameras were used for the marker-based and markerless system setup. Both systems were synchronized and recorded simultaneously. The keypoint trajectories from Blazepose were obtained by feeding the images collected with GoPros as an input to the algorithm. Further analysis included calibration, 3D reconstruction, and data filtering in Matlab. Skeletal modeling and joint angle calculations were conducted in OpenSim for both systems to eliminate the methodological difference. SPM1D Matlab package was used for statistical analysis. Significant differences were observed in ankle and hip joint angles between the Blazepose and Vicon systems at specific gait cycle phases in both the CP and TD groups. The ankle angle showed significant differences in the CP group at 0.7–1.3% (p<0.016) of the gait cycle and in the TD group at 38–46% (p<0.016). For hip flexion, significant differences in the CP group were noted at 0.81–1.81% (p<0.016), 13–40% (p<0.016), and 89–93% (p<0.016) of the gait cycle, while in the TD group, a significant difference was observed at 75–84% of the gait cycle (p<0.016). No significant difference was observed for the knee angle in both groups. The results of this study highlight the potential use and certain limitations of markerless motion capture systems like Blazepose in clinical settings. While showing promise in certain aspects of joint angle tracking, the study emphasizes the need for more refined datasets and advanced algorithms to enhance the accuracy and reliability of such systems, especially for clinical applications involving CP patients.
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This study compares the markerless motion capture (Blazepose) to the gold standard marker-based system (Vicon) by assessing the hip, knee, and ankle joint flexion angles of cerebral palsy (CP) patients and their typically developed (TD) peers. The participant group included six CP patients and six TD individuals. The participants walked on an 8-meter gait path at a self-selected pace. 11 Vicon cameras (200 Hz) and 3 GoPro (60 Hz) cameras were used for the marker-based and markerless system setup. Both systems were synchronized and recorded simultaneously. The keypoint trajectories from Blazepose were obtained by feeding the images collected with GoPros as an input to the algorithm. Further analysis included calibration, 3D reconstruction, and data filtering in Matlab. Skeletal modeling and joint angle calculations were conducted in OpenSim for both systems to eliminate the methodological difference. SPM1D Matlab package was used for statistical analysis. 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spellingShingle Mustafaoglu, Afet Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy markerless motion capture clinical gait analysis Biomekaniikka Biomechanics 5012 syväoppiminen liikeoppi CP-vammaiset CP-oireyhtymä deep learning kinematics cerebral palsied cerebral palsy
title Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy
title_full Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy
title_fullStr Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy
title_full_unstemmed Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy
title_short Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy
title_sort feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy
title_txtP Feasibility of markerless motion capture in clinical gait analysis in children with cerebral palsy
topic markerless motion capture clinical gait analysis Biomekaniikka Biomechanics 5012 syväoppiminen liikeoppi CP-vammaiset CP-oireyhtymä deep learning kinematics cerebral palsied cerebral palsy
topic_facet 5012 Biomechanics Biomekaniikka CP-oireyhtymä CP-vammaiset cerebral palsied cerebral palsy clinical gait analysis deep learning kinematics liikeoppi markerless motion capture syväoppiminen
url https://jyx.jyu.fi/handle/123456789/92224 http://www.urn.fi/URN:NBN:fi:jyu-202312088223
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