One-Leg Standing: Right and Left Balance COP and EMG Analyses
Bibliographic record
Abstract
Manual asymmetries in motor control have been studied for more than 120 years, with the bulk of this work focusing on unimanual control of upper limb movements. Given that the global population is aging, and that gait and balance disorders in this population are common causes of falls, it is critical to understand control mechanisms underlying balance. To this end, the current study investigates lateralization control mechanisms underlying one leg balance using the mean distribution and variability in center of pressure (COP) in 109 right-hand dominant individuals. Participants stood on a force platform with either the left leg, right leg, or both legs under two visual conditions (eyes open and closed). Electromyography (EMG) was recorded from four lower leg muscles (tibialis anterior, peroneus longus, soleus, and the medial head of the gastrocnemius) in 15 participants. Preliminary data analysis indicates that center of pressure (COP) distribution and variability and overall muscle activity is higher in eyes-closed conditions. In addition, asymmetries in postural stability control were observed, with higher COP variability in the single left leg than right leg in both anterior-posterior and medial-lateral directions. higher EMG activity during left leg standing for both visual conditions. In future work, we will examine potential differences in the lateralization of postural control in aging populations and in populations with impaired postural control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".