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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".