Searching for Stability as we Age: The PCA-Biplot Approach
Bibliographic record
Abstract
Principal component analysis (PCA) has been successfully applied to gait data; however, interpretation of the components is challenging. An alternative is to use a graphical display called biplot that gives insights into relationships and trends of data sets. Our goal was to demonstrate the sensitivity of gait variables to aging in elderly women with PCA-biplot. One hundred fifty-one elderly females (71.6±5.0 yrs), 152 adults (44.7±5.4 yrs) and 150 young (21.7±4.1 yrs) participated in the study. Gait spatial and temporal parameters were collected using a computerized carpet. PCA-biplot, discriminant analysis and MANOVA were used in the analysis. PCA-biplot revealed that elderly females walked with lower velocity, shorter step length, reduced swing time, higher cadence, and increased double support time compared to the other two groups. The greatest distances between the groups were along the variable step length with the elderly group showing a decrease of 8.4 cm in relation to the younger group. The discriminant function confirmed the importance of principal component 2 for group separation. Because principal component 2 was heavily weighted by step length and swing time, it represents a measure of stability. As women age they seek a more stable gait by decreasing step length, swing time, and velocity. PCA-biplot highlighted the importance of the variable step length in distinguishing between women of different age groups. It is well-known that as we age we seek a more stable gait. The PCA-biplot emphasized that premise and gave further important insights into relationships and trends of this complex data set.
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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 | 0.006 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".