Knowledge Transfer to Improve sEMG Simultaneous Proportional Movement Detection: A Data Transformation Approach
Bibliographic record
Abstract
Simultaneous proportional detection (SPD), based on surface electromyography (sEMG) signals, is a promising approach that achieves higher performance when trained on complex movements. However, collecting such training data is time-consuming for the user. To enable real-world applications, it is crucial to balance precision and ease of use. This study proposes a data transformation approach to enhance detection performance by transferring knowledge from a pre-existing dataset of complex movements to a simpler movement dataset. So, instead of asking a user to perform a variety of movements each session, a pre-recorded set of complex wrist movements data, including star-shaped trajectories, is used to add richness to a simpler set of wrist movement data requested from the user to perform movements only in X and Y. A linear transformation model is proposed to transfer movement knowledge between datasets, improving accuracy while maintaining the advantages of transfer learning. Experimental results confirm that the proposed knowledge transformation model improves R2performances by 5–10%. Additionally, the approach reduces data acquisition time, enhancing practicality for real-world applications in human-machine interaction, while being effective for both inter-session and inter-subject scenarios.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".