Investigation of Optimum Pattern Recognition Methods for Robust Myoelectric Control During Dynamic Limb Movement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The control of upper limb prostheses based on surface electromyogram (EMG) pattern recognition has long been the focus of many researchers as an important clinical option for amputees. More recently, it has been shown that changes induced during use, such as changes in limb position and performing dynamic activities, can have a substantial impact on the robustness of EMG pattern recognition. This work investigates whether there are alternative EMG features and classifiers which can outperform the commonly used time domain (TD) features and linear discriminant analysis (LDA) classifier in the context of limb positional changes and performing dynamic activities of daily living. A variety of EMG feature combinations and popular classifiers are compared in this study. The bases of comparison are classification accuracy and class separability. The results showed that adding Willison amplitude (WAMP) feature to the commonly used TD feature set combined with LDA classifier reduces the averaged absolute classification error by 1.4%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it