PolarFitts: A More Intuitive Framework for Assessing Myoelectric Control Performance
Bibliographic record
Abstract
Fitts' Law provides a mathematical framework for evaluating human-machine interfaces using metrics of movement time and throughput. Although intended as a pointing task, it has been widely adopted in other paradigms such as myoelectric prosthesis control, where prosthesis functions are traditionally mapped to a Fitts-style Cartesian coordinate system. In this study, we compare using: 1) Fitts' Law to assess classificationand regression-based myoelectric control in both this traditional Cartesian system, and 2) a new polar coordinate system, called PolarFitts. Eight participants trained a classification and a regression myoelectric control model by performing a series of muscle contractions (hand open/close, forearm pronation/supination, and rest). They then completed trials of both the Fitts and PolarFitts tests using the models. PolarFitts yielded superior control performance and reduced mental demand and frustration, for both models while using the same underlying mechanics. This suggests that PolarFitts may be a better and more intuitive coordinate system for evaluating control of the degrees of freedom often used in state-of-the-art prosthesis 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.008 | 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".