Skip to content
DraconiticResearch
Search Sources API
203,952 studies31 sourcesUpdated 19 min ago
Peer-reviewedCC BY 4.0Scientific Reports

Agreement of markerless and marker-based joint kinetics in team sport-specific change-of-direction movements

Abstract

Markerless (ML) motion capture is a practical alternative to marker-based (MB) systems, yet its ability to estimate joint kinetics during team sport-specific movements remains unclear. This study evaluated the agreement between ML and MB lower extremity joint moment estimates during change-of-direction (COD) tasks. Nineteen team sport athletes performed straight running and four COD movements (45-, 90-, 135-, 180-degrees) at different speeds. Joint moments at the hip, knee, and ankle were computed from synchronized force-plate data and either ML video data (Theia3D) or MB motion-capture data. Agreement was assessed using an extended Bland–Altman approach with linear mixed-effects models for peak joint moments and bootstrapped functional prediction bands for the entire stance phase. Substantial random and systematic differences were observed between systems exceeding the defined threshold of ≤ 10% in relative limits of agreement (LoAs) and in average MB signal range × 100% stance. LoAs for peak joint moments ranged from ± 0.38 Nm/kg at the ankle to ± 1.21 Nm/kg at the hip, indicating that difference between systems was of similar magnitude to, or larger than, the underlying MB-derived peak moments. Relative random differences ranged from 92 to 128% across movement planes. Agreement did not show a consistent pattern across movement directions or direction-specific speed categories. These findings indicate that ML- and MB-derived joint kinetics cannot be used interchangeably.

Related

  1. Evaluation of a markerless data augmentation algorithm for sports movements
    Axel Koussou, Hanane Ariouat, Sebastien Laporte +1
    Multidisciplinary Biomechanics JournalPeer-reviewedCC BY 4.0Full textGeneral physicsHAL
  2. Feature-dependent agreement between markerless and marker-based motion capture during continuous Taijiquan movement
    Minghua Wang, Yuchen Zhang
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textCrossref
  3. Concurrent Validity and Agreement of Viewpoint-Dependent Markerless Motion Tracking in Upper-Limb Assessment: A Validation Against Optoelectronic Motion Capture
    Suman Shrestha, Yuichiro Honda, Haruki Toda +2
    Sensors (Basel, Switzerland)Peer-reviewedCC BYFull textEurope PMC
  4. Is Markerless Motion capture accurate enough to identify kinematic risk factors associated with the ACL rupture during high-velocity change of direction? A pilot study on rugby players.
    Sébastien Davidovici, Marie Le Moing, Adryan Bakhouche +4
    Multidisciplinary Biomechanics JournalPeer-reviewedCC BY 4.0Full textSurgery & anaesthesiaHAL
  5. Spacetime Existence Theory——Why does light travel in a specific direction
    shuwei Ping
    ZenodoPreprintCC BY 4.0Full textZenodo
BibTeX
@article{helwig2026agreement,
  title = {{Agreement of markerless and marker-based joint kinetics in team sport-specific change-of-direction movements}},
  author = {Janina Helwig and Jos Vanrenterghem and Daniel Koska and Albert Gollhofer and Steffen Willwacher},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-72910-3},
  url = {https://www.nature.com/articles/s41598-026-72910-3},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Agreement of markerless and marker-based joint kinetics in team sport-specific change-of-direction movements
AU  - Janina Helwig
AU  - Jos Vanrenterghem
AU  - Daniel Koska
AU  - Albert Gollhofer
AU  - Steffen Willwacher
PY  - 2026
DA  - 2026/10/07
JO  - Scientific Reports
DO  - 10.1038/s41598-026-72910-3
UR  - https://www.nature.com/articles/s41598-026-72910-3
AB  - Markerless (ML) motion capture is a practical alternative to marker-based (MB) systems, yet its ability to estimate joint kinetics during team sport-specific movements remains unclear. This study evaluated the agreement between ML and MB lower extremity joint moment estimates during change-of-direction (COD) tasks. Nineteen team sport athletes performed straight running and four COD movements (45-, 90-, 135-, 180-degrees) at different speeds. Joint moments at the hip, knee, and ankle were computed from synchronized force-plate data and either ML video data (Theia3D) or MB motion-capture data. Agreement was assessed using an extended Bland–Altman approach with linear mixed-effects models for peak joint moments and bootstrapped functional prediction bands for the entire stance phase. Substantial random and systematic differences were observed between systems exceeding the defined threshold of ≤ 10% in relative limits of agreement (LoAs) and in average MB signal range × 100% stance. LoAs for peak joint moments ranged from ± 0.38 Nm/kg at the ankle to ± 1.21 Nm/kg at the hip, indicating that difference between systems was of similar magnitude to, or larger than, the underlying MB-derived peak moments. Relative random differences ranged from 92 to 128% across movement planes. Agreement did not show a consistent pattern across movement directions or direction-specific speed categories. These findings indicate that ML- and MB-derived joint kinetics cannot be used interchangeably.
SN  - 2045-2322
ER  -