Novel Control Strategies for Arm Prostheses : A Partnership between Man and Machine
Bibliographic record
Abstract
The human hand is an exquisite instrument. Most importantly, it is capable of performing a wide range of functions, and is able to switch seamlessly from one function to the next in response to the changing environment. Not surprisingly, the hand’ s loss through amputation is a major disability with drastic impact on quality of life. Prosthetic devices are designed to help alleviate some of the negative consequences of hand and arm amputation. Early prosthetic devices were purely mechanical, as exemplified by the traditional hook-and-cable systems. Later prostheses began to incorporate electrical and mechanical components to perform functions, and could be controlled by muscle signals in an amputee’ s residual limb （Fig. 1） . Termed myoelectric control, the use of muscle signals for control of robotic prostheses has gained increasing popularity since the 1960’ s. 1,2） Recent technological advances in sensor and actuator technology have further enabled the development of sophisticated myoelectric prostheses. These advances include myoelectric hands with multiple functions, or grip patterns, that amputees can select to perform various tasks. 3）
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".