Knee kinematic signals clustering for the Identification of sagittal and transverse gait patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study is to investigate knee kinematic signals clustering by principal component analysis. The aim is to identify meaningful patterns in normal gait knee flexion/extension and tibial internal/external rotation signals. To preserve all of the information contained in these kinematics signals, the analysis uses the entire angle curve over a gait cycle rather then a few features extracted from this curve as done traditionally. To reduce processing complexity, the data dimensionality is reduced without loss of relevant information by projecting the gait curve onto a subspace of significant principal components (PCs). Gait patterns are then extracted by a discriminant analysis of the set of training data based on the PCs sign. The analysis identified two representation patterns for each of the flexion/extension (sagital plane) and the tibial internal/external rotation (transverse plane). These patterns were validated both by the clustering silhouette width and clinical interpretation.
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.
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 | 0.000 | 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 it