Stacked one-vs-one (SOvO): A new approach for multi-class classification for sEMG recognition
Bibliographic record
Abstract
Significant advancements have been made in applying machine learning techniques to the classification of surface electromyography (sEMG) signals. Despite these improvements, there remains considerable room for enhancing these methods to achieve even greater accuracy. This is particularly evident in complex sEMG classification problems, where the patterns across different classes can be very similar. This similarity often leads to reduced accuracy in both validation and test sets, highlighting the need for more refined approaches. This study aims to improve sEMG-based hand gesture recognition by introducing a novel Stacked One-vs-One (SOvO) classification approach. Traditional One-vs-One (OvO) strategies decompose multi-class problems into binary classifications but often suffer from cumulative errors of weak classifiers. The proposed SOvO method enhances classification by stacking the probability outputs from pairwise binary classifiers in the OvO approach on the training data as additional features for the final classification. Depending on whether the final classifier is a deep learning or conventional model, different feature integration strategies were explored. A comparative analysis was conducted on the Ninapro DB4, DB5, and DB6 datasets across two window sizes. Results show that SOvO consistently outperforms OvO in accuracy by 3% to 10%. This improvement is statistically significant, as confirmed by a repeated measures ANOVA ( p < 0.001). These findings demonstrate that SOvO provides a more robust and accurate framework for sEMG-based gesture recognition, addressing limitations of conventional OvO methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".