Marker-based classification of young–elderly gait pattern differences via direct PCA feature extraction and SVMs
Why this work is in the frame
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Bibliographic record
Abstract
The classification of gait patterns has great potential as a diagnostic tool, for example, for the diagnosis of injury or to identify at-risk gait in the elderly. The purpose of the paper is to present a method for classifying group differences in gait pattern by using the complete spatial and temporal information of the segment motion quantified by the markers. The classification rates that are obtained are compared with previous studies using conventional classification features. For our analysis, 37 three-dimensional marker trajectories were collected from each of our 24 young and 24 elderly female subjects while they were walking on a treadmill. Principal component analysis was carried out on these trajectories to retain the spatial and temporal information in the markers. Using a Support Vector Machine with a linear kernel, a classification rate of 95.8% was obtained. This classification approach also allowed visualisation of the contribution of individual markers to group differentiation in position and time. The approach made no specific assumptions and did not require prior knowledge of specific time points in the gait cycle. It is therefore directly applicable for group classification tasks in any study involving marker measurements.
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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 | 0.001 | 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) | 0.000 | 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 it