Toward a realistic optoelectronic-based kinematic model of the hand: representing the transverse metacarpal arch reduces accessory rotations of the metacarpophalangeal joints
Bibliographic record
Abstract
A kinematic model representing the versatility of the human hand is needed to evaluate biomechanical function and predict injury risk in the workplace. We improved upon an existing optoelectronic-based kinematic hand model with grouped metacarpals by defining segmented metacarpals and adding the trapeziometacarpal joint of the thumb. Eight participants performed three static postures (neutral pose, cylinder grip, cap grip) to evaluate kinematic performance of three different models, with one, two, and four metacarpal segment(s). Mean distal transverse metacarpal arch angles in the four-segment metacarpal model were between 22.0° ± 3.3° (neutral pose) and 32.1° ± 3.7° (cap grip). Representation of the metacarpals greatly influenced metacarpophalangeal joint rotations. Both the two- and four-segment metacarpal models displayed significantly lower metacarpophalangeal joint 'supination' angles (than the one-segment model) for the fourth and fifth fingers. However, the largest reductions were for the four- versus one-segment models, with mean differences ranging from 9.3° (neutral pose) to 17.0° (cap grip) for the fourth finger and 16.3° (neutral pose) to 33.0° (cylinder grip) for the fifth finger. MCP joint abduction/adduction angles of the fourth and fifth fingers also decreased with segmentation of the metacarpals, although the lowest magnitudes generally occurred in the four-segment model. Overall, the four-segment metacarpal model produced the lowest accessory rotations in non-dominant axes, and best matched previous radiological studies that found MCP joint pronation/supination angles were typically less than 10°. The four-segment metacarpal model, with improved anatomic fidelity, will better serve future studies of detailed actions of the hand in clinical or work applications.
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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) | 0.001 | 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) | 0.001 | 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".