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Record W2023138654 · doi:10.1115/1.2354202

Muscle Tuning During Running: Implications of an Un-tuned Landing

2006· article· en· W2023138654 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Biomechanical Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSIGNAL (programming language)HeelCompartment (ship)VibrationAccelerationSoft tissueSoft landingMovement (music)AcousticsBiomedical engineeringComputer sciencePhysicsAnatomyGeologyMedicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The impact force in heel-toe running is an input signal into the body that initiates vibrations of the soft tissue compartments of the leg. These vibrations are heavily damped and the paradigm of muscle tuning suggests the body adapts to different input signals to minimize these vibrations. The objectives of the present study were to investigate the implications of not tuning a muscle properly for a landing with a frequency close to the resonance frequency of a soft tissue compartment and to look at the effect of an unexpected surface change on the subsequent step of running. METHOD: Thirteen male runners were recruited and performed heel-toe running over two surface conditions. The peak accelerations and biodynamic responses of the soft tissue compartments of the leg along with the EMG activity of related muscles were determined for expected soft, unexpected hard and expected hard landings. RESULTS AND CONCLUSIONS: For the unexpected hard landing there was a change in the input frequency of the impact force, shifting it closer to the resonance frequency of the soft tissue compartments. For the unexpected landing there was no muscle adaptation, as subjects did not know the running surface was going to change. In support of the muscle-tuning concept an increase in the soft tissue acceleration did occur. This increase was greater when the proximity of the input signal frequency was closer to the resonance frequency of the soft tissue compartment. Following the unexpected change in the input signal a change in pre-contact muscle activity to minimize soft tissue compartment vibrations was not found. This suggests if muscle tuning does occur it is not a continuous feedback response that occurs with every small change in the landing surface properties. In previous studies with significant adaptation periods to new input signals significant correlations between the changes in the input signal frequency and the EMG intensity have been shown, however, changes in soft tissue accelerations have not been found. The results of the present study showed that changes in these soft tissue accelerations can occur in response to a resonance frequency input signal when a muscle reaction has not happened.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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