The Performance of Onset Detection Methods for Surface Electromyographic Data
Bibliographic record
Abstract
The performance of five different algorithm-based onset detection methods for surface electromyography (SEMG) data was analyzed relative to visually determined onset times from three expert volunteers. It was hypothesized that at least one algorithm would out-perform the others in terms of degree of correctness. Three-hundred data plots from three previous studies on motor control were selected as source data. The automated algorithms tested included: i) a forward-moving sliding window, considering window mean value and number of points above a threshold [12], ii) a backward-moving sliding window starting at the data peak, considering window mean value compared to a threshold (developed by authors), iii) a backward-moving sliding window starting at the highly smoothed data peak, considering window mean value and number of points below a threshold (developed by authors), iv) a forward-moving sliding window, considering window mean value and number of points above a threshold lasting for a minimum number of consecutive windows [13], and v) a system based on log likelihood ratios and maximum likelihood estimation [11]. Each method went through parameter optimization as part of the testing. The three expert volunteers determined onset times by visual inspection for the 300 data plots on two occasionsforeachoftwofiltermethods:a2ndorder Butterworth filter with 6 Hz cut-off frequency, and a 20 ms sliding window root-mean-square (RMS) filter. The algorithms were ranked based on i) a Z value, expressing the average of each data plot’s deviation from the visually determined onset distribution and ii) having no more than 10% erroneous results. The forward moving sliding window, considering window mean value and number of points above a threshold [12], resulted in the most accurate determination of onset time, with an RMS averaged Z value of 0.7738. However, the algorithms were comparable and several had unique advantages.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".