A Constitutive Model to Characterize In Vivo Human Palmar Tissue
Bibliographic record
Abstract
In vivo characteristics of palmar soft tissue can be used to improve the accuracy of human models to explore and simulate a range of contact scenarios. Tissue characteristics can help to assess injury prevention strategies and designing technologies that depend on quantified physical contacts such as prosthetics, wearables, and assistive devices. In this study, a simplified quasi-linear viscoelastic (QLV) model was developed to quantify large deformation, in vivo soft tissue relaxation characteristics of the palm. We conducted relaxation tests on 11 young adults (6 males, 5 females, 18 < age < 30, mean age: 25 ± 4 yr) and 9 older adults (6 males, 3 females, age > 50, mean age: 61.5 ± 11.5 yr) using a 3 mm indenter to a depth of 50% of each participant's soft tissue thickness. The relaxation parameters of the QLV model were found to differ with age and sex, emphasizing the importance of using targeted material models to represent palmar soft tissue mechanics. Older adults showed on average 2.3-fold longer relaxation time constant compared to younger adults. It took 1.2-fold longer for young males to reach equilibrium than for young females; however, young females had a higher level of relaxation (36%) than young males (33%). Differences in specific QLV model parameters, P1, P2, and α were also found between age and sex groups. QLV characteristics differentiated by age and sex, add biofidelity to computational models which can provide a better representation of the diversity of tissue properties in the population.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".