Prior-Prompt-Based GCN for Depression Recognition Through Gait Observation
Bibliographic record
Abstract
In recent years, depression, as a prevalent mental health disorder has drawn increasing attention. With the advance of AI technology, automatic and objective diagnosis methods emerge by observing signals like electroencephalogram (EEG) signals, faces and behaviors. In the present paper, we propose gait analysis as a non-invasive method for depression detection. In this study, we propose a prior-prompt-based graph convolution network (PP-GCN) for depression recognition through gait that integrates skeleton and text modalities. Different from the conventional single-modal methods in the present study, we utilize prior knowledge and angle features. We innovatively introduce Generative Action Prompt (GAP), leveraging a pre-trained large language model to generate motion descriptions for different body parts, thereby providing prior knowledge for depression recognition. Additionally, considering the subtle gait feature variations in individuals with depression, we further incorporate a joint-angle-based representation strategy to capture fine-grained variations in movements. Experimental results demonstrate that the proposed model outperforms existing skeleton-based approaches on a large-scale dataset which contains over 25,000 gait sequences from nearly 300 volunteers named D-Gait, achieving excellent performance.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".