Personalized Myoelectric Control for Upper-Limb Exoskeletons Through Meta-Learning: A Few-Shot Learning Approach
Bibliographic record
Abstract
Personalization in the myoelectric control of robotic exoskeletons is crucial to ensuring accurate interpretation and adaptation to the unique muscle activity patterns and movement intentions of each user. This approach minimizes the risk of incorrect or excessive force application, significantly reducing the likelihood of user discomfort or injury during operation. This study introduces a model-agnostic meta-learning approach for personalizing a soft upper-limb exoskeleton in industrial settings. The framework incorporates an attention-based CNN-LSTM model that predicts future angular positions of the robot using EMG and IMU signals. The MAML framework demonstrates significant adaptability and personalization, efficiently predicting future angular positions with minimal data, approximately 20-25 seconds per task. This approach effectively reduces the necessity for extensive retraining with new users or in new environments by 50%, showcasing real-time task adaptation capabilities. Our findings confirmed a reduced human effort of nearly 13% in load-bearing tasks. Also, the results show that the exerted torque from the exoskeleton was 24% higher while maintaining higher accuracy. A comparison with other deep learning models further emphasizes the enhanced adaptability and accuracy offered by the meta-learning approach.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".