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Between-Day Reliability of Kinematic Variables Using Markerless Motion Capture for Single-Leg Squat and Single-Leg Landing Tasks
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Luis Llurda-Almuzara,
Richard Jones
International Journal of Sports Physical Therapy, Volume: 20, Issue: 8, Pages: 1160 - 1175
Swansea University Author:
Chelsea Starbuck
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DOI (Published version): 10.26603/001c.141870
Abstract
Background: Markerless motion capture has the potential to repeatedly collect biomechanical data during activities associated with injuries. Few studies have assessed reliability of this technology during single-leg tasks. Purpose: To examine the between-day reliability of trunk and lower limb kinem...
| Published in: | International Journal of Sports Physical Therapy |
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| ISSN: | 2159-2896 |
| Published: |
North American Sports Medicine Institute (NASMI)
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa69555 |
| Abstract: |
Background: Markerless motion capture has the potential to repeatedly collect biomechanical data during activities associated with injuries. Few studies have assessed reliability of this technology during single-leg tasks. Purpose: To examine the between-day reliability of trunk and lower limb kinematics during single-leg squat and single-leg landing tasks using markerless motion capture. To examine the between-day reliability of the same protocol using marker-based motion capture. Design: Reliability. Methods: Nineteen recreational athletes performed all tasks in two sessions, one week apart. Joint angles of trunk, hip, knee, and ankle were processed using Theia3D. A separate study (10 different participants) evaluated the reliability of marker-based motion capture. Full curve analysis was examined using root mean square difference (RMSD) and statistical parametric mapping (SPM) and discrete point analysis (initial contact and peak knee flexion) using intraclass correlation coefficient (ICC), and standard error of measurement (SEM). Results: For full curve analysis, markerless motion capture demonstrated low mean RMSD for all joints and planes in both SLS (3.6˚±1.3˚) and landing tasks (forward=3.2˚±1.3˚; medial=3.4˚±1.7˚). SPM showed statistical difference for bilateral hip flexion (full curve) and unilateral hip adduction, rotation, and knee flexion during a percentage of landing tasks. For discrete point analysis, ICC mean indicated moderate to good reliability (SLS= 0.77; forward landing= 0.83; medial landing= 0.80) with low mean SEM values (SLS=3.1°±1.3˚; forward landing=2.3˚±0.9°; medial landing=2.3˚±0.8˚). Marker-based motion capture showed slightly higher mean RMSD (SLS=4.2˚±1.8˚; forward landing=3.5˚±1.0˚; medial landing=3.3˚±0.9) and SEM values (SLS=4.1˚±2.2˚; forward landing=2.7˚±1.2°; medial landing=2.8˚±1.2˚). ICC mean showed good relative reliability (SLS=0.90; forward landing=0.88; medial landing=0.88). Hip flexion presented values >5° across tasks and technologies (5° to 8°). Conclusions: Markerless motion capture using Theia3D can reliably measure single-leg tasks with measurement errors comparable to marker-based motion capture. The low measurement error provides confidence for the regular monitoring of athletes during single-leg tasks. |
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| Keywords: |
repeatability, measurement error, pose estimation, deep learning |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was supported by the University of Salford and Machine Learning in Athletics. |
| Issue: |
8 |
| Start Page: |
1160 |
| End Page: |
1175 |