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Record W3109378353 · doi:10.18280/i2m.190511

Measurement and Feature Analysis of Plantar Pressure Center in Athletes under Different Exercise Modes

2020· article· en· W3109378353 on OpenAlexvenueno aff
Jie Yang

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsForefootHeelPlantar pressureFoot (prosody)Center of pressure (fluid mechanics)Physical medicine and rehabilitationAthletesGaitGround reaction forceBalance (ability)Physical therapyTrajectoryMedicineEngineeringSurgeryPressure sensorKinematicsPhysicsAnatomy

Abstract

fetched live from OpenAlex

Walking and running, two essential exercises in daily training for athletes, are major causes to foot injuries. The plantar pressure center (PPC) can accurately reflect the gait process, and effectively measure the function of foot. This paper measures the PPC trajectories of a total of 45 athletes during the support period of walking and running, and analyzes their regularities under different exercise modes. The time percentage of PPC trajectory and foot progression angle were selected as the main observation indices. The support period under each exercise mode was divided into four phases: the initial contact phase (ICP), the forefoot contact phase (FFCP), the foot flat phase (FFP), and the forefoot push off phase (FFPOP). The statistical analysis shows that: Under the running mode, the time of the heel landing on the ground and the relative load on the heel decrease with the growing speed. Under the same exercise mode, the left and right feet differ slightly in the PPC trajectory; the foot progression angle peaks in FFPOP and minimizes in FFP. In ICP and FFPOP, the foot progression angle under running is smaller than that under walking; in FFP and FFCP, the foot progression angle under running is larger than that under walking. The research results provide the scientific basis for the reasonable arrangement of athlete training.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.291
Teacher spread0.250 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueInstrumentation Mesure MétrologieSame topicDiabetic Foot Ulcer Assessment and ManagementFrench-language works237,207