Quantitative and objective full-field strain measurements of healthy human skin during distal upper extremity range of motion
Bibliographic record
Abstract
Traditional tools for assessing burns, such as the Vancouver Scar Scale and Cutometry, provide only subjective or localized mechanical evaluations. These methods lack the capacity to deliver comprehensive, objective data on skin deformation, particularly during dynamic movement. This study aims to demonstrate the utility of 3D-Digital Image Correlation (3D-DIC) as a non-invasive, quantitative technique for capturing full-field strain and displacement responses of the skin throughout the complete articular range of motion of the distal upper extremity. An in vivo experimental protocol was applied to a healthy subject to evaluate the mechanical behavior of the skin on the dorsal hand and volar forearm. 3D-DIC was used to quantify strain and displacement during metacarpophalangeal flexion (MF), composite fist (CF), wrist flexion (WF), and wrist extension (WE). Cutometry was also applied on volar and dorsal regions of the forearm to obtain comparative strain profiles under localized suction. 3D-DIC revealed semicircular skin recruitment patterns. Maximum displacements of 9 mm and 11 mm were observed for MF and CF, respectively, with corresponding maximum strains of 30 % and 35 %. During WF and WE, displacements ranged from 10-12 mm, with the highest strain localized near the radiocarpal joint. While Cutometry and 3D-DIC yielded different strain behaviors, the two methods proved complementary, enhancing the understanding of skin deformation under different mechanical stimuli. These findings suggest that 3D-DIC can serve as a robust, objective tool for evaluating skin mechanics in clinical and research settings, especially for applications involving scar assessment, surgical planning, and rehabilitation monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".