THE INFLUENCE OF DIFFERENT-HEIGHT HEEL SHOES ON MOTOR FUNCTION OF LOWER LIMB JOINTS IN THE YOUNG FEMALE PERFORMERS
Bibliographic record
Abstract
Objective: This study aims to explore how shoes with different height heels affect female gait characteristics and motor function of lower limb joints. Methods: Video analysis and myoelectricity tester were applied to the walking test of 12 female models wearing shoes with 0, 3, 6, 10, and 18[Formula: see text]cm heels. Results: (1) When being barefoot and wearing the flat shoes, the models took a longest step, and the step length decreased with the increase of the shoe heel. (2) When walking in the flat shoes, the models kept the center of gravity highly stable. With the increase of the shoe heel, the center of gravity went ups and downs obviously in the direction of Z and Y when models were walking. (3) When models walked in the flat shoes, the smallest changes occurred in the hip joint angle. With the increase of the shoe heel, the stretching ranges of knee joint angle and ankle joint angle decreased. (4) When models walked in the flat shoes, electromyographic mean power frequency (MPF) indicated that active frequency of gastrocnemius and soleus were the highest and time-domain parameter suggested that active scope of biceps femoris and soleus increased most. There was difference in active frequency between the dominated leg and the nondominated leg. Conclusion: Flat shoes or 3–6[Formula: see text]cm heel shoes were the best for walking. It is recommended to choose shoes with a heel height higher than 10[Formula: see text]cm when walking,and try to control wear more than 10[Formula: see text]cm heel walking time, otherwise, there will be a risk of falls. When choosing a heel with a height higher than 10[Formula: see text]cm, the walking speed and walking length must be reduced. At the same time, try to control the walking time of wearing high heels with the heel height over 10[Formula: see text]cm, otherwise it will cause the risk of fall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".