The effect of reducing the number of EMG channel inputs on loading and stiffness estimates from an EMG-driven model of the spine
Bibliographic record
Abstract
Electromyography (EMG)-driven models of the spine routinely require between ten and 14 EMG channels to estimate joint load and stiffness variables. This study was designed to determine the sensitivity of common EMG-driven model outputs to the removal of individual EMG channels, and to test two adapted models driven from eight channels. A total of 11 male participants performed a variety of static exertions designed to resist either an applied trunk flexion or right side trunk lateral bend moment. In this study, 14 channels of EMG were recorded and used to drive a biomechanical model of the spine to predict L4-L5 joint load and stiffness values. The model was subsequently re-run after the removal of individual pairs of bilateral EMG channels, and again with eight-channel models in which the rectus abdominus, latissimus dorsi and multifidus EMG-channels were eliminated. Results showed that the eight-channel model provided estimates for the majority of output variables that did not differ substantially from the 14-channel model, except in instances in which muscle force output was ramped to resist flexion moments. Estimates of the output variables were, in general, improved when multifidus fascicles were re-added to the model and driven from the lumbar erector spinae EMG sites.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".