The influence of passive wrist joints on the functionality of prosthetic hands
Bibliographic record
Abstract
Background: The addition of a passive wrist to a single degree of freedom prosthesis has an effect on its functionality. The amount of impact is undetermined. Objectives: To measure the grasping function of a commercial single degree of freedom hands with and without two forms of passive wrist flexor. Study Design: Form-board and timed tasks. Method: Repeated measures with a single subject using a validated assessment tool. The test measured the function of one conventional, single axis, powered hand controlled by the same myocontroller format. It was used in conjunction with a passive three position wrist flexor, a wrist with compliance in the flex/extend and radial/ulnar deviation and compared with a hand without these axes. Results: The overall functional score of the hand alone was 80 out of 100. The use of a wrist flexion unit resulted in a higher score (83) and the compliant wrist achieved a score of 79. The addition of a wrist allowed improved performance in Power, Lateral and Tips grips for both wrist designs, in addition the Extension grip was improved with the compliant wrist. Conclusion: Wrist flexion had a positive impact on the functional score. It enabled some tasks to be performed quicker and with less difficulty. Clinical relevance The majority of prosthetic hands have pro/supination yet there is little evidence about what impact this, or any other motion at the wrist, has on the overall functional capabilities of a prosthetic hand (i.e. grip). A commercial myoelectric prosthetic hand when used with and without two different wrist designs can potentially improve grip performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".