Position Identification for Robust Myoelectric Control Against Electrode Shift
Bibliographic record
Abstract
The vulnerability to the electrode shift was one of the key barriers to the wide application of pattern recognition-based (PR-based) myoelectric control systems outside the controlled laboratory conditions. To overcome this challenge, a novel framework named position identification (PI) was proposed. In the PI framework, an anchor gesture performed by the user was first analyzed to identify the current electrode position from a pool of potential electrode shift positions. Next, the classifier calibrated by the data of the identified position would be selected for following myoelectric control tasks. The results of the amputee and able-bodied participants both demonstrated that the differential filter combined with majority voting improved the PI accuracy. With only one second contraction of the chosen anchor gesture (hand close), the subsequent PR-based myoelectric control performance was fully restored from eight different electrode shift scenarios, with 1 cm in either or both perpendicular and parallel directions. The classification accuracies with PI framework were not significant before and after the shift ( 0.001). The advantage of restoring performance fully in just one second made it a practical solution to improve the robustness of PR-based myoelectric control systems in a wide range of real-world applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".