Study on the Reliability of SemGCN in Gait Analysis
Bibliographic record
Abstract
The purpose of research is to analyze the gait parameters of figure in the video when he is not 90 degrees to the camera position through the trained SemGCN model, and analyze its reliability. Two groups of video data in the experiment are collected in the same time, meaning experimental group and reference group. In the video data of experimental group, the angle between figure and camera position is 120 degrees, while it is 90 degrees in the video data of reference group. The 2D joint point coordinate information of video data in experimental group is obtained firstly through 2D network, and then it is delivered to trained SemGCN model to regress 3D model and extract 3D joint point coordinate information, then output knee angle curve, which would be processed for filtering and smoothing by Savitzky-Golay wave filter. The video data in reference group is directly processed through Openpose algorithm, and the knee angle curve is output. Finally, the relevance of data in two groups are analyzed by adopting intraclass correlation coefficient(ICC). The result shows that the correlation of a single measurement is 0.898, meaning the data in two groups are strongly correlated. The reliability of gait parameters of figure in the video which are analyzed by using SemGCN model is good, which can be applied for the measurement of figure joint angle in video or other relevant fields.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".