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Record W4415553254 · doi:10.1123/jab.2024-0221

Multijoint Coordination Contributes to the Minimization of Frontal Plane Center-of-Mass Displacement in Maximal Velocity Sprinting

2025· article· en· W4415553254 on OpenAlexaff
Chris L. Vellucci, Shawn M. Beaudette

Bibliographic record

VenueJournal of Applied Biomechanics · 2025
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsBrock University
Fundersnot available
KeywordsSprintCoronal planeBiomechanicsTrunkKinematicsDisplacement (psychology)Work (physics)Excursion

Abstract

fetched live from OpenAlex

Previous studies on sprinting biomechanics have identified a variety of biomechanical characteristics that describe the outcome of improved sprint technique but in doing so have neglected to identify the postural control strategies that lead to improved sprint performance. The purpose of this study was to evaluate the relationship between frontal plane displacement of the center of mass (CoM) and sprint velocity, and to understand if multijoint coordination of the limbs and trunk were associated with minimizing the displacement. The results from this study suggest that stance phase frontal plane CoM cumulative path length is significantly associated with sprint velocity. Further, a multivariate linear regression model revealed that coordinative couplings of the bilateral limbs and trunk were associated with the minimization of the frontal plane CoM displacement. Specifically, the coordination of the knees (flexion-extension) and axial rotation of the thorax-pelvis were the most important interjoint couplings in minimizing frontal plane CoM displacement. Frontal plane CoM displacement provides coaches, athletes, and performance professionals with an interpretable metric to identify sprint technique quality in field using wearable sensors. Future work needs to further advance our understanding of postural control during sprinting so that effective coaching and rehabilitation interventions can be designed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.204
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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