MétaCan
Menu
Back to cohort
Record W3157198103 · doi:10.82308/16415

Study of soft exoskeleton with elastic assistance on ice hockey forward skating acceleration

2018· article· en· W3157198103 on OpenAlexfundno aff
Brian D. McPhee

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
FundersMcGill University
KeywordsIce hockeyExoskeletonAccelerationSpeed skatingGeologyComputer scienceAeronauticsSimulationPhysical medicine and rehabilitationEngineeringPhysicsMedicineClassical mechanics

Abstract

fetched live from OpenAlex

This Master's study examined the performance effects of a soft exoskeleton designed to store and return elastic energy about the hip joint during ice hockey skating starts. Three elastic resistance conditions ("Soft", "Medium", "Stiff") were examined. Kinematic, kinetic and perception data were collected on nine male high calibre hockey players during skating start accelerations over nine meters. This study demonstrated the feasibility of introducing soft exoskeletons to store and return elastic energy to an athlete's body while skating on ice. Several participants responded well, on average yielding 1.54% shorter times during skating start performance (p=0.052). Gross skating kinetic and kinematic patterns were unaltered, though reduced stride length and double support times were counter balanced by higher cadence. Based on individual time performance and preference measures, specific elastic resistances need to be tailored to each athlete in order to optimize their performance. Further study is warranted with larger sample sizes, both male and female athletes, longer familiarization periods, and on different skating tasks.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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.

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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicWinter Sports Injuries and PerformanceFrench-language works237,207