Comparing shoulder muscle activity in symptomatic and asymptomatic groups: the influence of normalization technique
Bibliographic record
Abstract
Maximum voluntary contractions (MVCs) are the standard normalization method for muscle activity, but can be hindered by pain and injury. Submaximal normalization may be a viable option. The study objective was to compare muscle activation between symptomatic and asymptomatic groups with MVC and submaximal normalization to determine if similar relative between-groups differences could be detected. Eighteen participants, divided into symptomatic and asymptomatic groups, performed isometric MVCs and six dynamic functional tasks. EMG data were normalized using MVC and submaximal values from a weighted overhead lift. MVCs achieved higher activation levels for most muscles, but submaximal normalization provided comparable values for serratus anterior. Significant between-group differences were observed during the Comb Hair, with higher activation in the symptomatic group for the upper trapezius, middle trapezius, and supraspinatus across both normalizations. The serratus anterior during the Overhead Reach and lower trapezius in the Tie Apron were also different between groups with both normalizations. There were some significant findings that emerged from only one normalization method. Submaximal normalization may be a viable alternative to MVC normalization for select muscles and upper limb pathological populations. Submaximal normalization allowed for meaningful comparisons of muscle activation patterns during functional tasks without the need for maximum force exertion.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".