A Layered Deformable Model for Gait Analysis
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a layered deformable model (LDM) is proposed for human body pose recovery in gait analysis. This model is inspired by the manually labeled silhouettes in (Z. Liu, et al., July 2004) and it is designed to closely match them. For fronto-parallel gait, the introduced LDM model defines the body part widths and lengths, the position and the joint angles of human body using 22 parameters. The model consists of four layers and allows for limb deformation. With this model, our objective is to recover its parameters (and thus the human body pose) from automatically extracted silhouettes. LDM recovery algorithm is first developed for manual silhouettes, in order to generate ground truth sequences for comparison and useful statistics regarding the LDM parameters. It is then extended for automatically extracted silhouettes. The proposed methodologies have been tested on 10005 frames from 285 gait sequences captured under various conditions and an average error rate of 7% is achieved for the lower limb joint angles of all the frames, showing great potential for model-based gait recognition
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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 | 0.000 | 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