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Record W4409448706 · doi:10.1080/10255842.2025.2490139

Effect of foot-shaped bionic shoes on ground reaction forces and foot stress at various running speeds

2025· article· en· W4409448706 on OpenAlexaff
Shunxiang Gao, Dong Sun, Yang Song, Xuanzhen Cen, Qiaolin Zhang, Zixiang Gao, Zhiyi Zheng, Monèm Jemni, Yaodong Gu

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
FundersK. C. Wong Magna Fund in Ningbo UniversityNatural Science Foundation of Ningbo
KeywordsFoot (prosody)Ground reaction forceStress (linguistics)Physical medicine and rehabilitationMaterials scienceMedicinePhysicsArtClassical mechanicsKinematics

Abstract

fetched live from OpenAlex

This study examined ground reaction forces(GRFs) and bone stress differences between bionic running shoes (with foot-mimicking soles) and traditional shoes during running.Sixteen experienced male runners ran at 10, 12, and 14 km/h in both shoe types. Two-way ANOVA and SPM1d showed that bionic shoes had significantly lower peak propulsive but higher peak braking forces than traditional shoes.Bionic shoes also exhibited lower vertical forces in early stance and altered anterior-posterior forces patterns in late stance; finite element analysis indicated lower metatarsal stress in the bionic midsoles. These findings provide insights for designing footwear to prevent running injuries.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.011
GPT teacher head0.293
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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