A Novel Framework Based on Position Verification for Robust Myoelectric Control Against Sensor Shift
Bibliographic record
Abstract
This study presented a novel framework to improve the robustness of pattern recognition-based myoelectric control algorithms against sensor shift, which was one of the obstacles for its practical applications outside controlled laboratory conditions. Different from the previous proposed methods, which mostly focused on improving the classification performance in the shift condition, this framework provided the functionality of verifying if the sensor position was shifted. If so, the user was enabled to adjust the sensor position before it affecting the following control. We demonstrated that one verification attempt could be completed in a short time (<; 2 s) with a high accuracy (equal error rate, or EER<; 5%). The control performance was theoretically proved to be improved after position verification. The simulated results of the experimental data from six sensors around the arm showed that with improved discrete Fourier transform (iDFT) feature, in most scenarios, smaller than five verification attempts (<; 10 s) were needed to correct the sensor position from 1-cm shift perpendicular to muscle fibers. The performance after correction was comparable to that of a traditional method including additional training data from the expected shift positions. The proposed framework dealt with the sensor shift problem from the perspective of efficient position correction, potentially expanding the application of myoelectric control, especially with armband, in industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".