A robust measure of probability density function of various noises in electromyography (EMG) signal acquisition
Bibliographic record
Abstract
Statistical methods for estimating a probability density function (PDF) of surface electromyography (EMG) signals during upper-limb motions have been investigated in previous studies to select the suitable feature extraction methods for multifunction myoelectric control systems. While these methods have achieved a good performance in estimating PDF of EMG signals from different motions and muscles, no prior studies have evaluated the performance of these methods to estimate the PDF of noises in EMG signal acquisition. The utility of these methods consisting of bicoherence, kurtosis, negentropy, and L-kurtosis, was investigated in estimating the PDF of five different noise types: the single and many spurious background spikes, white Gaussian noise, motion artifact, and power line interference. The results show that the L-kurtosis can identify the PDF of all studied noises in EMG signal acquisition correctly. In contrast, other estimating methods are inaccuracy in at least one noise type.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".