An Adaptive Classification Methodology for Myoelectrically Controlled Prostheses
Bibliographic record
Abstract
Myoelectric signals (MES) have proven to be effective inputs to control systems of powered prosthetic devices. A number of output motions can be derived from the MES monitored from multiple control sites by employing pattern recognition techniques; however, MES measurement conditions will change over time, causing increased signal variation from the training data, making initial training data inadequate classification exemplars. To create a dynamically adaptable system, a classifier that undergoes continuous online training was developed. This classifier validates decisions and uses valid feature vectors for retraining, with classification decisions as classifier targets. Validation utilizes a retraining buffer to find 64 consecutive and identical majority vote decisions. The use of a large buffer ensures a higher confidence that the class decisions are correct. Every 8^th feature vector from the buffer is incorporated into the training set, discarding older feature vectors to maintain a constant number of training exemplars. Retraining the classifier with this new training set allows the classifier to adapt to changes in the MES. This study compared the continuously trained linear discriminant analysis classifier with a noncontinuously trained classifier, using data collected from six subjects. An average improvement of 2.57% was seen with the continuously trained classifier.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".