Vision-Based Freezing of Gait Detection With Anatomic Directed Graph Representation
Bibliographic record
Abstract
Parkinson's disease significantly impacts the life quality of millions of people around the world. While freezing of gait (FoG) is one of the most common symptoms of the disease, it is time consuming and subjective to assess FoG for well-trained experts. Therefore, it is highly desirable to devise computer-aided FoG detection methods for the purpose of objective and time-efficient assessment. In this paper, in line with the gold standard of FoG clinical assessment, which requires video or direct observation, we propose one of the first vision-based methods for automatic FoG detection. To better characterize FoG patterns, instead of learning an overall representation of a video, we propose a novel architecture of graph convolution neural network and represent each video as a directed graph where FoG related candidate regions are the vertices. A weakly-supervised learning strategy and a weighted adjacency matrix estimation layer are proposed to eliminate the resource expensive data annotation required for fully supervised learning. As a result, the interference of visual information irrelevant to FoG, such as gait motion of supporting staff involved in clinical assessments, has been reduced to improve FoG detection performance by identifying the vertices contributing to FoG events. To further improve the performance, the global context of a clinical video is also considered and several fusion strategies with graph predictions are investigated. Experimental results on more than 100 videos collected from 45 patients during a clinical assessment demonstrated promising performance of our proposed method with an AUC of 0.887.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".