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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 OpenAlex
Shunxiang Gao, Dong Sun, Yang Song, Xuanzhen Cen, Qiaolin Zhang, Zixiang Gao, Zhiyi Zheng, Monèm Jemni, Yaodong Gu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.011
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