Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems
Abstract Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the validity of kinematic and kinetic parameters...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-02739-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849704456665432064 |
|---|---|
| author | Changzhi Yang Linyu Wei Xi Huang Lili Tu Yanjia Xu Xiaolong Li Zhe Hu |
| author_facet | Changzhi Yang Linyu Wei Xi Huang Lili Tu Yanjia Xu Xiaolong Li Zhe Hu |
| author_sort | Changzhi Yang |
| collection | DOAJ |
| description | Abstract Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the validity of kinematic and kinetic parameters derived from ML for lower limb joints requires further validation in populations engaged in high-intensity jumping sports. The purpose of this study was to compare lower limb kinematic and kinetic estimates between marker-based (MB) and ML motion capture systems during jumps. Fourteen male Division I movement collegiate athletes performed a minimum of three squat jumps (SJ), drop jumps (DJ), and countermovement jumps (CMJ) in a fixed sequence. The movements were synchronized using ten infrared cameras, six high-resolution cameras, and two force measurement platforms, all controlled by Vicon Nexus software. Motion data were collected, and the angles, moments, and power at the hip, knee, and ankle joints were calculated using Theia3D software. These results were then compared with those obtained from the Vicon system. Comparative analyses included Pearson correlation coefficients (r), root mean square differences (RMSD), extreme error values, and statistical parametric mapping (SPM).SPM analysis of the three movements in the sagittal plane revealed significant differences in hip joint angles, with joint angle RMSD ≤ 5.6°, hip joint moments RMSD ≤ 0.26 N·M/kg, and power RMSD ≤ 2.12 W/kg showing considerable variation, though not reaching statistical significance. ML systems demonstrate high measurement accuracy in estimating knee and ankle kinematics and kinetics in the sagittal plane during these conventional jump tests; however, the accuracy of hip joint kinematic measurements in the sagittal plane requires further validation. |
| format | Article |
| id | doaj-art-9a4d9daf4129466f839c7a237d3cafb6 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9a4d9daf4129466f839c7a237d3cafb62025-08-20T03:16:46ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-02739-9Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systemsChangzhi Yang0Linyu Wei1Xi Huang2Lili Tu3Yanjia Xu4Xiaolong Li5Zhe Hu6Shandong Sport UniversitySchool of Physical Education, Southwest Medical UniversitySchool of Physical Education, Southwest Medical UniversitySchool of Physical Education, Southwest Medical UniversityDepartment of Physical Education, Jeonbuk National UniversityDepartment of Physical Education, Chengdu Sport UniversitySchool of Physical Education, Southwest Medical UniversityAbstract Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the validity of kinematic and kinetic parameters derived from ML for lower limb joints requires further validation in populations engaged in high-intensity jumping sports. The purpose of this study was to compare lower limb kinematic and kinetic estimates between marker-based (MB) and ML motion capture systems during jumps. Fourteen male Division I movement collegiate athletes performed a minimum of three squat jumps (SJ), drop jumps (DJ), and countermovement jumps (CMJ) in a fixed sequence. The movements were synchronized using ten infrared cameras, six high-resolution cameras, and two force measurement platforms, all controlled by Vicon Nexus software. Motion data were collected, and the angles, moments, and power at the hip, knee, and ankle joints were calculated using Theia3D software. These results were then compared with those obtained from the Vicon system. Comparative analyses included Pearson correlation coefficients (r), root mean square differences (RMSD), extreme error values, and statistical parametric mapping (SPM).SPM analysis of the three movements in the sagittal plane revealed significant differences in hip joint angles, with joint angle RMSD ≤ 5.6°, hip joint moments RMSD ≤ 0.26 N·M/kg, and power RMSD ≤ 2.12 W/kg showing considerable variation, though not reaching statistical significance. ML systems demonstrate high measurement accuracy in estimating knee and ankle kinematics and kinetics in the sagittal plane during these conventional jump tests; however, the accuracy of hip joint kinematic measurements in the sagittal plane requires further validation.https://doi.org/10.1038/s41598-025-02739-9Athletic performanceSports injuryJumpingBiomechanicsLower limb jointsStatistical parametric mapping |
| spellingShingle | Changzhi Yang Linyu Wei Xi Huang Lili Tu Yanjia Xu Xiaolong Li Zhe Hu Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems Scientific Reports Athletic performance Sports injury Jumping Biomechanics Lower limb joints Statistical parametric mapping |
| title | Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems |
| title_full | Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems |
| title_fullStr | Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems |
| title_full_unstemmed | Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems |
| title_short | Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems |
| title_sort | comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker based motion capture systems |
| topic | Athletic performance Sports injury Jumping Biomechanics Lower limb joints Statistical parametric mapping |
| url | https://doi.org/10.1038/s41598-025-02739-9 |
| work_keys_str_mv | AT changzhiyang comparisonoflowerlimbkinematicandkineticestimationduringathletejumpingbetweenmarkerlessandmarkerbasedmotioncapturesystems AT linyuwei comparisonoflowerlimbkinematicandkineticestimationduringathletejumpingbetweenmarkerlessandmarkerbasedmotioncapturesystems AT xihuang comparisonoflowerlimbkinematicandkineticestimationduringathletejumpingbetweenmarkerlessandmarkerbasedmotioncapturesystems AT lilitu comparisonoflowerlimbkinematicandkineticestimationduringathletejumpingbetweenmarkerlessandmarkerbasedmotioncapturesystems AT yanjiaxu comparisonoflowerlimbkinematicandkineticestimationduringathletejumpingbetweenmarkerlessandmarkerbasedmotioncapturesystems AT xiaolongli comparisonoflowerlimbkinematicandkineticestimationduringathletejumpingbetweenmarkerlessandmarkerbasedmotioncapturesystems AT zhehu comparisonoflowerlimbkinematicandkineticestimationduringathletejumpingbetweenmarkerlessandmarkerbasedmotioncapturesystems |