Reducing the effects of muscle fatigue on upper limb myoelectric control using adaptive LDA
Bibliographic record
Abstract
Muscle fatigue is considered one of the main causes of sEMG changes during repetitive contractions performed for long periods of time. In the current work we are proposing and evaluating an approach in order to reduce the effects of muscle fatigue on upper limb myoelectric control using adaptive LDA. A dataset of surface EMG signals from nine subjects, including six normally-limbed and three upper limb amputees, was processed. The EMG signal was encoded using four time-domain features and four coefficients of an auto-regressive model. Adaptive and non-adaptive strategies were compared using Accuracy, False Positive Rate, Sensitivity and F1 score. Results obtained with normally-limbed subjects show that in normal scenario while muscle fatigue increases, the recognition accuracy and Sensitivity of the classifier decrease from more than 90 % to less than 58 %; False Positive Rate increases from around 9 % to 36.2 %, and F1-score decreases from 0.9 to 0.6. In contrast, parameters maintain a more stable and higher performance when adaptive LDA is evaluated. Although control in amputees shows a reduction in performance compared with normally-limbed subjects, results show a similar trend. The Wilcoxon sum rank test shows a significant increase in performance of upper limb myoelectric control tasks when adaptive LDA is used. The main limitation of this work is the need of supervision in the adaptation procedure to decide if a trial is adequate for retrain the model, making the proposed method difficult to implement in a real scenario. Future work is needed in order to obtain a parameter that serves to choose the proper trial for model retraining.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".