The Effects of Different Kinds of Smooth Pursuit Exercises on Center of Pressure and Muscle Activities during One Leg Standing
Bibliographic record
Abstract
This study examined the effects of gaze fixation and different kinds of smooth-pursuit eye movements on the trunk and lower extremity muscle activities and center of pressure. METHODS: Twenty-four subjects were selected for the study. The activity of trunk and lower limb muscles (tibialis anterior, lateral gastrocnemius, medial gastrocnemius, vastus midialis obliques, vastus lateralis, biceps femoris, rectus abdominis, and erector spinae) and the COP (center of pressure) (surface area ellipse, length, and average speed) were measured to observe the effects of gaze fixation and different kinds of smooth-pursuit eye movements on the center of pressure and muscle activities during one leg standing. Before the experiment, a Gaze point GP3 HD Eye Tracker (Gazept, Vancouver, BC, Canada) was used to train eye movement so that the subjects would be familiar with smooth eye movement. Repeated each exercise 3 times at random. In order to avoid the sequence deviation caused by fatigue, the movement sequence is randomly selected. RESULT: The center of pressure and muscle activities were increased significantly when the smooth-pursuit eye movement with one leg standing compared with gaze fixation with one leg standing. In smooth-pursuit eye movements, the changes in the center of pressure and muscle activities were increased significantly with eye and head movement. When the head and eyes moved in opposite directions, the center of pressure and muscle activities were increased more than with any other exercises. CONCLUSION: Smooth-pursuit eye movement with one leg movement affects balance. In particular, in the smooth-pursuit eye movement with one leg standing, there were higher requirements for balance when the eyes and head move in the opposite direction. Therefore, this movement can be recommended to people who need to enhance their balance ability.
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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) | 0.001 | 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 | 0.000 |
| 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".