Transfer Learning Strategies for Leveraging Multi-Session and Multi-Subject Datasets to Improve sEMG Recognition
Bibliographic record
Abstract
Building movement detection models based on surface electromyography (sEMG) requires extensive data collection, which is time-consuming due to inter-session and inter-subject variability in the EMG signals. In fact, sensor placement can differ across sessions, and muscle physiology and user experience vary between subjects. The model-based approach of transfer learning offers a solution by transferring knowledge from an already acquired dataset into a model and deriving this pre-trained model to achieve good performance for a new session or a new subject with fewer data. However, how can this approach effectively exploit datasets from multiple sessions and multiple subjects? Compatibility issues arise between some pre-trained models and data from different sessions or subjects, affecting prediction accuracy. This study addresses these challenges and proposes strategies for combining and selecting transfer learning models from diverse datasets that significantly improve the prediction of eight forearm gestures with minimal training data. The proposed strategies produce promising results, with the combination strategy exceeding 90% precision and others between 85 and 90%.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".