Improving simultaneous and proportional control from EMG signals based on a Two-Stage Regression Structure
Bibliographic record
Abstract
A two-stage regression structure is proposed in this paper to improve simultaneous and proportional control based on electromyography (EMG) signals. Instead of considering the conventional approach with a regression model per degree of freedom (DoF), the proposed method applies a regression model per direction of each DoF: the 1ststage detects DoFs and then is used to select the direction models of the 2ndstage. By using linear regression on the data from one healthy experienced participant for 2 DoFs of his wrist, the proposed structure was evaluated offline with cross-evaluation and online with a real-time control of a cursor to hit some targets. The evaluation results showed a clear improvement of the proposed method in terms of accuracy, ability to reach the boundaries, reaction speed, accurate control and reactive control compared to the conventional approach. The potential of this structure also lies in the fact that it can use different regression methods, work for 3 DoFs and more and use the 1ststage knowledge to improve performance of the 2ndstage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".