Functional mapping of multiple mechanomyographic signals to hand kinematics
Bibliographic record
Abstract
Current methods of prosthesis control are overly simplistic, using two opposing electromyographic control signals to control the prosthetic hand, normally with no finger control. Recent research has demonstrated that mechanomyographic (MMG) signals can do the same. This experiment investigates the possibility of fusing multiple MMG signals to create a control scheme for prostheses that provides control over groups of fingers. Concurrent recordings of MMG activity and finger motions were made during several finger movements. The recordings were used to determine the functional mapping that exists between MMG signals and finger motions, and to implement a basic classification system. Two hyperplanes were able to separate thumb from finger flexion with 87% accuracy and pinkie from middle three finger flexion with 82% accuracy. Overall accuracy was 76.2%. Further improvements can be made over this linear classifier, and are worth further study. The results indicate that MMG may be used to enhance accuracy in remote manipulation 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".