Investigating Feedback-Informed Screen-Guided Training to Enhance Myoelectric Control and Predictability
Bibliographic record
Abstract
Screen-guided training is a widely used method for calibrating myoelectric prostheses, wherein users follow visual prompts. However, this approach often fails to capture the complexities of real-world usage when the user is actively engaging with the controller. This study, therefore, aimed to develop an alternative training protocol that promotes more robust pattern recognition-based myoelectric control. In an experiment with 20 participants, we compared three training methods: conventional screen-guided training without feedback, real-time visual feedback of principal component analysis (PCA)-based projections of EMG activity, and real-time classification feedback with intentionally corrupted classifier outputs. After training, participants completed a Fitts' law-style target acquisition task in a virtual environment, repeating it at three different difficulty levels. We then evaluated how offline accuracy and metrics, particularly Bhattacharyya Distances computed from combinations of the PCA projections, correlated with online control performance. Our findings indicate that training with feedback yielded the best performance, with PCA-based visual feedback providing the most effective calibration environment. Additionally, projecting the EMG data collected with PCA-based feedback into the PCA space derived from the no-feedback data improved the correlation between offline separability metrics and the online Fitts' Law throughput. Interestingly, this correlation was stronger for the easy difficulty level. Nevertheless, the benefits of PCA-based feedback were consistent across the three different difficulty levels of the Fitts' law task, it as a beneficial and robust approach worthy of further exploration.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".