The effect of myoelectric prosthesis control strategies and feedback level on adaptation rate for a target acquisition task
Bibliographic record
Abstract
The long-term performance of myoelectric prostheses is related not only to the short-term performance of the controller, but also to the user's ability to learn and adapt to the system. Different control architectures may have inherent tradeoffs between their short-term performance and the amount of relevant feedback that informs this adaptation. In this study we focused on the ability of two common types of myoelectric control interfaces: raw control with raw feedback, such as a regression, and filtered control with filtered feedback, such as a classifier, to affect user adaptation. We evaluated trial-by-trial adaptation to self-generated errors during a multi degree-of-freedom target acquisition task by fitting a linear regression model to data collected from 24 able-bodied subjects. Subjects showed significantly higher adaptation behavior to self-generated errors when using raw control with a raw feedback strategy than when using filtered control with a filtered feedback strategy, which suggests that control strategies with more feedback allow for higher adaptation. These results support our hypothesis that feedback-rich control strategies allow users to better understand the myoelectric control system, which may enable better long-term performance.
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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 | 0.020 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".