A comparison of task and muscle specific isometric submaximal electromyography data normalization techniques
Bibliographic record
Abstract
The submaximal, constrained nature of joystick manipulation makes it difficult to select an appropriate technique for upper limb electromyography (EMG) normalization. The purpose of this study was to determine an appropriate submaximal isometric normalization method to quantify EMG from shoulder muscle activation in hydraulic-actuation joystick operators that could later be implemented in field settings. Surface EMG data were collected from the upper trapezius, posterior deltoid, and anterior deltoid of seventeen subjects while operating a hydraulic-actuation joystick. EMG data were normalized using two techniques: muscle specific (mRVC) and task specific (three joystick positions: start-tRVCStart, middle-tRVCMiddle and end-tRVCEnd). No significant differences (p ⩽ 0.05) were observed for intersubject coefficient of variation (CV) between normalization procedures (mRVC, tRVCStart, tRVCMiddle tRVCEnd, un-normalized). These equivocal findings do not favour the use of any one of the submaximal normalization procedures over another. However, though not statistically significant, the un-normalized (0.68 ± 0.15) CVs were lower than those of normalized ensembles (0.96 ± 0.24) suggesting that for constrained, submaximal tasks, it may not be necessary to normalize EMG. Although this analysis was applied to upper limb EMG during joystick manipulation, the results have potential application to other submaximal upper limb tasks which are constrained and repetitive in nature thus including many assembly line jobs.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".