Changes in Muscle Activity in Response to Different Impact Forces Affect Soft Tissue Compartment Mechanical Properties
Bibliographic record
Abstract
Electromyographic (EMG) activity is associated with several tasks prior to landing in walking and running including positioning the leg, developing joint stiffness and possibly control of soft tissue compartment vibrations. The concept of muscle tuning suggests one reason for changes in muscle activity pattern in response to small changes in impact conditions, if the frequency content of the impact is close to the natural frequency of the soft tissue compartments, is to minimize the magnitude of soft tissue compartment vibrations. The mechanical properties of the soft tissue compartments depend in part on muscle activations and thus it was hypothesized that changes in the muscle activation pattern associated with different impact conditions would result in a change in the acceleration transmissibility to the soft tissue compartments. A pendulum apparatus was used to systematically administer impacts to the heel of shod male participants. Wall reaction forces, EMG of selected leg muscles, soft tissue compartment and shoe heel cup accelerations were quantified for two different impact conditions. The transmissibility of the impact acceleration to the soft tissue compartments was determined for each subject/soft tissue compartment/shoe combination. For this controlled impact situation it was shown that changes in the damping properties of the soft tissue compartments were related to changes in the EMG intensity and/or mean frequency of related muscles in response to a change in the impact interface conditions. These results provide support for the muscle tuning idea--that one reason for the changes in muscle activity in response to small changes in the impact conditions may be to minimize vibrations of the soft tissue compartments that are initiated at heel-strike.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".