Development of Underactuated Prosthetic Fingers with Joint Locking and Electromyographic Control
Bibliographic record
Abstract
Modern hand prostheses possess a large number of degrees of freedom. These degrees of freedom cannot simply be actuated by a single motor each, since their combined size and weight would exceed the limitations of an anthropomorphic prosthesis. Some hand prostheses try to remedy this by way of underactuation of the fingers or addition of entirely passive fingers, but this reduces the hand's ability to execute different grasp types. We present a joint locking system, allowing certain degrees of freedom to be fixed during actuation of an underactuated finger. These locks are actuated by miniature solenoids, and allow the fingers to support a variety of grasp types. In this paper, these locks are implemented in a two-fingered prosthesis prototype, which is able to perform several grasping motions important for prosthesis users. This prototype is controlled by pre-recorded electromyographic signals, which control different grasp types and their opening/closing. Various grasping experiments show that the prototype is able to execute three essential grasp types for daily living with a single main actuator, and can be intuitively controlled by means of six different electromyographic signals. This prototype demonstrates new joint locking mechanisms and control systems that can provide an anthropomorphic, myoelectric hand prosthesis with minimal actuation and intuitive control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".