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Record W4408489535 · doi:10.1504/ijbet.2024.10070029

The effect of Fatigue on Lower Limb Coordination Characteristics in Badminton forehand smash: a Functional Principal Component Analysis

2024· article· en· W4408489535 on OpenAlexaff
Zixiang Gao, Zhou Zhanyi, Shudong Li

Bibliographic record

VenueInternational Journal of Biomedical Engineering and Technology · 2024
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPrincipal component analysisComponent (thermodynamics)Physical medicine and rehabilitationComputer scienceLower limbMedicineArtificial intelligenceSurgeryPhysics

Abstract

fetched live from OpenAlex

The study of motor coordination explores how the central nervous system controls body movements. Using functional principal component analysis (FPCA), this study examined the impact of fatigue on limb coordination synergies and activity coefficients in 23 badminton players performing forehand smashes before and after fatigue. Kinematic data revealed that pre-fatigue, three coordination synergies effectively controlled all lower limb joints, while post-fatigue, four synergies were required, reflecting increased control complexity. Fatigue significantly altered synergy control: Synergy 2's control of the hip joint decreased, control of the right ankle increased (p < 0.05), and Synergy 3's control of the left ankle decreased (p < 0.05). Fatigue also shifted synergy activity, enhancing Synergy 3 control during landing while reducing Synergy 1 control in the follow-through. These findings highlight how fatigue modifies coordination strategies, increasing joint control complexity, with implications for training to enhance performance stability and reduce injury risks.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.264
Teacher spread0.258 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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