Rejection of Systemic and Operator Errors in a Real-Time Myoelectric Control Task
Bibliographic record
Abstract
In myoelectric pattern-recognition control, the rejection of movement decisions based on confidence - the likelihood of a correct classification - has been shown to improve system usability, however it is not known to what extent this is due directly to error mitigation, and to what extent this is due to users having opportunities to change the way they contract. To understand this, 24 subjects participated in a real-time pattern recognition control task with rejection at seven different confidence thresholds, and without rejection. Errors were classified into systemic errors (i.e., those produced by the classifier) and operator errors (i.e., those produced by user behavior). It was found that the error permitted by the rejection controller was reduced by about half at high rejection thresholds, with both systemic and operator errors significantly affected, while the errors produced by the user remained essentially constant throughout. Conversely, correct decisions were filtered out by the rejection controller at significantly greater rates at high rejection thresholds, which may be excessive enough to ultimately impair usability. While some subjects reported being experienced in myoelectric control, no significant differences were observed due to experience level.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".