A comparison of isometric and isokinetic normalization methods for electromyographic data from sub-regions of supraspinatus and infraspinatus during dynamic tasks
Bibliographic record
Abstract
This study explored effects of using isometric versus isokinetic maximal voluntary contractions (MVCs) to normalize EMG data from supraspinatus and infraspinatus subregions during isokinetic tasks. Participants performed submaximal isokinetic external rotation (ER) and scaption tasks at two speeds. Three isometric MVCs were used: seated ER; side-lying scaption; side-lying abduction. Isokinetic MVCs were performed in the same position and speeds as the experimental tasks. Data were normalized using peak EMG from reference tasks: MVC which produced the greatest amplitude overall (MEA), isometric MVC with greatest amplitude (isometric best), isokinetic MVC with greatest amplitude (isokinetic best), and the greatest amplitude from the isokinetic MVC that matched the experimental task (isokinetic matched). Mean %MVC from each experimental task/ sub-region were compared by normalization method. The isokinetic matched method versus the MEA method was significantly different in all comparisons with isokinetic matched resulting in relative normalized task values up to 162% greater. The isometric best method resulted in significantly greater %MVC 37% of the time compared to the MEA method, whereas there were no differences when using isokinetic best compared to MEA. Isokinetic MVCs are less likely to overestimate %MVC than isometric and their use should be considered when normalizing data from dynamic tasks.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".