Comparison of EMG activity during stable and unstable push‐up protocols
Bibliographic record
Abstract
Abstract This experiment examined muscle activation measured using electromyography (EMG) during a standardized push‐up performed on stable and unstable surfaces. Fifteen highly trained participants performed four push‐ups: standard (hands and feet on the floor), either the hands or feet on an unstable surface (single instability), and with both hands and feet on unstable surfaces (dual instability). Unstable surfaces were created using a stability ball and an extreme balance board. EMG activity was recorded from three core stabilizers (erector spinae, rectus abdominus and internal obliques), one prime mover (triceps), and one lower body stabilizer (soleus). The EMG time series were smoothed using a 10‐point moving average and root mean squares (RMS) were calculated for the entire time series. The results showed that push‐ups performed with dual instability had significantly greater EMG activation compared to single instability or the stable push‐up. In addition, as instability increased, there was a greater amount of muscle activation for the core stabilizers, prime movers and lower body stabilizers. The findings are consistent with the position that unstable surfaces in conjunction with standard exercises can be used to increase activation of core trunk stabilizers. This may in turn provide increased trunk strength and greater resistance to injury.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".