Novel Features for EMG Pattern Recognition Based on Higher Order Crossings
Bibliographic record
Abstract
In this work, we present a novel set of higher order time domain features for surface electromyographic (EMG) pattern recognition. The proposed methods employ simple measures of frequency information extracted from EMG time series when a sequence of differencing filters is applied. Multiple EMG datasets consisting of 48 able-bodied and transradial amputee subjects performing a large variety of hand and fingers movements are used to evaluate the performance and robustness of the proposed features. The results show that these novel higher order-based features provide significantly better performance than their traditional counterparts by 3-15 % (p <; 0.05). The best proposed feature, higher-order myopulse percentage rate, also significantly outperformed other frequency information-based EMG features in the time and frequency domains: histogram, mean frequency, and median frequency, by 8-14%, 8-25%, and 14-35% (p <; 0.05), respectively. With relatively less computational complexity, the proposed features could potentially be used as new features for extracting frequency information for EMG- based pattern recognition systems.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".