Quantifying Forearm Soft Tissue Motion from Massless Skin Markers following Forward Fall Hand Impacts
Bibliographic record
Abstract
Background: Investigating soft tissue motion related to impact events is important for understanding how the body mitigates potentially injurious forces through shock attenuation. Objectives: The aims of this study were to: 1) quantify displacement and velocity of the forearm soft tissues following forward fall impacts; and 2) compare two massless skin marker designs (single layer, uniform (SLU) design; stacked, non-uniform (SNU) design) in terms of how well they could be tracked over varying skin pigmentations using automated motion capture software. Methods: Two participant groups (skin pigmentation: light – 9F, 8M; dark – 9F, 6M) underwent simulated forward fall hand impacts for each marker design using a torso-release apparatus. Marker positions associated with planar motion of forearm soft tissues during impact were automatically tracked (ProAnalyst®) in the proximal-distal and anterior-posterior axes from high speed recordings (5000 f/s). Mean peak displacements and velocities for eight forearm regions were then calculated (LabVIEW®). Results: Overall, soft tissue displacement and velocity increased from distal to proximal forearm regions. The greatest displacement (1.47 cm) and velocity (112.8 cm/s) occurred distally toward the wrist. Soft tissue impact responses between sexes did not differ, on average (p > 0.05). The SLU and SNU markers produced different kinematic values (p < 0.05); however, the magnitudes of, and consequently meaningfulness of these statistical differences for automatically tracking soft tissue motion, were negligible (displacement: ≤ 0.05 cm; velocity: ≤ 2.5 cm/s). Conclusions: Forearm soft tissue motion was successfully quantified for forward fall hand impacts; both marker designs were deemed functionally equivalent.
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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) | 0.002 | 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".