Real-time classification of muscle signals from self-selected intentional movements
Bibliographic record
Abstract
A novel method to automatically differentiate forearm movements has been proposed. The electromyography (EMG) signals were recorded from two muscle sites on the forearm in real-time. Two 2-dimensional feature spaces namely the natural logarithm of root-mean-square values (Log (RMS)), and the standard deviations of auto regressive model coefficients (Stdev (AR)) were created. The features were calculated within non-overlapping 0.2 second windows in real-time. The feature spaces were clustered using the fuzzy c-means algorithm [1]. The cluster multiplicities were investigated by five different cluster validity indices. Real-time EMG signal classification was achieved by calculating membership values. Log (RMS) performed superior to the Stdev (AR) feature space. The silhouette validity index provided the best cluster validity index in this study. On average, the proposed algorithm classified 4 movements with 92.7± 3.2% and 5 movements with 79.90%±16.8% accuracy. The algorithm also revealed the number of repeatable movements. It can also be adapted to daily variations in individual EMG signals.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".