Extracting silhouette-based characteristics for human gait analysis using one camera
Bibliographic record
Abstract
With the strong development of computer vision, health care and in-home monitoring systems are widely applied. Gait analysis is one of the main problems, which needs to be solved in such systems. Most of the recent researches implemented on 3D information of each walking person extracted from stereo cameras or devices with sensors, thus it leads to an increase in the computational cost and price. Therefore, we propose an approach for performing gait analysis using only one normal camera. This paper presents how characteristics are extracted from the walking person's silhouette for gait analysis, in detail, detecting abnormal gaits. Experiments are performed with normal gaits and three different types of anomaly, which consist of hunched back, left-right asymmetry, and sudden motion variation. The obtained results show that there is no case of omission or false detection, and our solution can be integrated into real-time systems.
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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 | 0.000 | 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) | 0.001 | 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".