Principal component analysis of the power developed in the flexion/extension muscles of the hip in able-bodied gait
Bibliographic record
Abstract
This study was undertaken to demonstrate how principal component analysis (PCA) can be used: (a) to detect the main functional structure of actions taken by hip extensors and flexors during two consecutive gait cycles of able-bodied subjects, and (b) to determine whether or not symmetrical behaviour exists between right and left hip muscle power activity. Twenty young, healthy male subjects walked along a 13 m path at a freely-chosen speed. Applying curve structure detection methods such as PCA to walking patterns can provide insight into the functional tasks accomplished by the lower limbs of able-bodied and disabled subjects. PCA was applied as a classification and curve structure detection method to hip sagittal muscle power calculated for the right and left lower limbs. Over 70% of the information provided by the first four principal components (PCs) was chosen for further biomechanical interpretation. PC1 for both right and left sides mainly described the action taken by the hip extensors/flexors corresponding to the vertical component of ground force on the respective limbs during mid-stance. Propulsion and limb preparation were identified as the second and third tasks attributed to right hip muscle power, while between limb co-ordination was recognised as the second and third functional tasks of the left hip extensors/flexors. Balance was identified as the fourth main functional contribution of the hip extensors/flexors at the right limb while for the left limb, these muscles were mainly responsible for preparing the limb to enter into new gait cycle. PCA was able to identify the four main functional contributions of hip sagittal muscle power during able-bodied gait. PCA was also able to examine the existence of functional asymmetry in gait by highlighting different task priorities at the hip level for the right and left lower limbs.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".