Vision-Based Fall Risk Assessment Through Attention Augmented Neural Encoding and Data Augmentation
Bibliographic record
Abstract
Falls among elderly individuals and other at-risk populations represent a significant public health concern, underscoring the need for proactive and accessible fall risk assessment methods. Although prior systems using wearable sensors or depth cameras have demonstrated potential, their widespread adoption is often constrained by user discomfort, setup complexity, and the high cost of specialized equipment such as Kinect. To address these limitations, we propose GTAE-FRA, a novel vision-based deep learning framework for fall risk assessment that leverages standard, widely available RGB cameras to record Five times Sit-To-Stand (FSTS) test videos. GTAE-FRA begins with a robust video processing pipeline that includes 3D pose estimation, noise filtering, and Dynamic Time Warping (DTW)-based matching to extract reliable Body Skeleton Key Point (BSKP) sequences. These sequences are then fed into GTrans, an attention-augmented encoder combining message passing aggregation mechanism with Transformer-based spatial-temporal modeling for nuanced feature extraction. To overcome limited data availability, six skeleton-oriented data augmentation strategies are applied, which significantly enhance the diversity and robustness of training samples. Experimental results on a dataset of 450 FSTS videos demonstrate the effectiveness of GTAE-FRA, achieving impressive performance with detection accuracy, weighted F1 score, macro F1 score, and AUC of 87.00%, 87.01%, 88.92%, and 97.96%, respectively. These results represent average improvements of 2.49%, 2.66%, 2.24% and 1.78% over baseline methods across the respective metrics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".