Midsole Properties Affect the Amplitude of Soft Tissue Vibrations in Heel–Toe Runners
Bibliographic record
Abstract
INTRODUCTION: Soft tissue vibrations can generate discomfort and may necessitate a greater energy demand to preserve an efficient motion in running. Vibration damping is thus of interest from a comfort and performance standpoint. Our purpose was to assess whether changes in midsole material affect the properties of (a) soft tissue vibrations and (b) myoelectric activity. METHODS: Two midsole conditions were compared. The control condition corresponded to a full ethylene-vinyl acetate foam midsole. The experimental condition was a bimaterial midsole with a material combination of viscous and viscoelastic materials. Twelve participants ran on an indoor track in both conditions while recording the longitudinal acceleration and the EMG activity of vastus medialis (VM) and gastrocnemius medialis (GM). Wavelet transforms were performed for EMG and acceleration signals to assess the intensity of the muscle activity at low and high frequencies (37-128 and 170-395 Hz, respectively) and to calculate the damping coefficient (D) for soft tissue vibrations. The soft tissue vibrations were also characterized by the peak of acceleration (apeak), the frequency of the power peak (fpeak), and the power of the soft tissue vibrations (PSD[8-55]). RESULTS: The variables apeak and PSD[8-55] decreased for VM and GM in the viscous condition. Before heel strike, low-frequency EMG activity decreased for VM, and high-frequency EMG activity tended to decrease for GM in the viscous condition. The damping D was reduced only for VM, and fpeak was unchanged. CONCLUSIONS: A more viscous midsole substantially reduced the amplitude of soft tissue vibrations, but not their frequency. Looking at individual results, it was noted that muscle activity was tuned in response to the acceleration input, and that the damping of soft tissue vibrations was affected by the intensity of muscle activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".