On the robustness of EMG features for pattern recognition based myoelectric control; A multi-dataset comparison
Bibliographic record
Abstract
The selection of optimal features has long been a subject of debate for pattern recognition based myoelectric control. Studies have compared many features, but have typically used small or constrained data sets. Herein, the performance of various features is evaluated using data from six previously reported data sets. The number of channels, the contraction dynamics (dynamic vs static), and classifier type all yielded significant interactions (p<;0.05) with the feature set. When using 8 channels, the addition of the tested features produced no improvement over a standard time domain (TD) feature set alone (p>0.05). When using fewer channels, however, autoregressive, Cepstral coefficients, Willison amplitude and sample entropy features all provided significant improvement during dynamic contractions (p<;0.05). The simple Willison amplitude is highlighted, showing that it can provide significant improvement when used as a replacement for any one of the standard TD features.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".