High‐pass filtering surface EMG in an attempt to better represent the signals detected at the intramuscular level
Bibliographic record
Abstract
Surface electromyography (EMG) is often used to represent activation profiles of the underlying musculature. The purpose of this study was to assess the potential of high-pass (HP) filtering to improve the matching of surface EMG signals to those signals recorded intramuscularly. EMG was recorded at the skin surface over the infraspinatus and supraspinatus muscles as well as from fine-wire electrodes placed in the infraspinatus, supraspinatus, and teres minor muscles. The surface EMG signals were HP-filtered at 18 cutoff frequencies (0-510 HZ in 30 HZ increments), and the time-histories were correlated with the signals from the wire electrodes. HP filtering did not significantly alter the correlated muscle activation waveform relationship between the surface and wire signals until cutoffs reached 240 HZ. HP filtering of the surface signals did not improve the representation of the muscle fiber-level activation profile, but the results suggest that enough information resides in the high-frequency components of the signal to reproduce the activation time-history profile of the muscle.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".