Design and evaluation of voluntary opening and voluntary closing prosthetic terminal device
Bibliographic record
Abstract
Body-powered prostheses use a cable-operated system to generate forces and move prosthetic joints. However, this control system can only generate forces in one direction, so current body-powered prehensor designs allow the user either to voluntarily open or voluntarily close the tongs. Both voluntary opening (VO) and voluntary closing (VC) modes of operation have advantages for certain tasks, and many end-users desire a terminal device that can switch between the two modes. However, such a terminal device must maintain the same thumb position (i.e., point of Bowden cable attachment) and movement direction in both modes in order to avoid the need to readjust the harness after every mode switch. In this study, we demonstrate a simple design that fulfills these requirements while allowing the user to switch easily between modes. We describe the design concept, describe a rugged split-hook prototype, provide specifications (size, weight, efficiency, etc.), and present a pilot study in which five subjects with intact arms and two subjects with amputation used the VO and VC split-hook prehensor to perform the Southampton Hand Assessment Procedure. Subjects performed an average of 4 to 7 (+/- 0.2) points better when they could choose to switch between modes on a task-by-task basis than when they were constrained to using only VO or VC modes.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".