Cost-Efficient Fall Risk Assessment With Attention Augmented Vision Machine Learning on Sit-to-Stand Test Videos
Bibliographic record
Abstract
Falls among the elderly and other at-risk populations pose a significant public health challenge, necessitating innovative methods to detect and intervene early. While automated fall risk assessment methods using wearable sensors or depth cameras have been proposed, the physical and psychological burden of wearing extra sensors, and the high cost of specialized equipment like Kinect, limits their practical adoption. To tackle this challenge, this paper presents a novel machine learning-based fall risk assessment approach calledFRAVM, which operates on Five times Sit-To-Stand (FSTS) test videos captured with standard, widely available cameras to identify individuals requiring fall prevention interventions. To enhance the practicality ofFRAVM, 3D pose estimation is applied to generate vision-rich 3D body keypoints, mitigating the challenges posed by restricted camera angles. Median-average filtering is used to reduce noise caused by video shaking and pose estimation inaccuracies, while a new Dynamic Time Warping (DTW)-based matching algorithm is designed to handle interference from irrelevant individuals appearing in the video. Furthermore, a novel Attention-augmented Spatial-Temporal Graph Convolutional Network (AST-GCN) is developed for reliably identifying the action in each frame, enabling accurate computation of key kinematic features for fall risk prediction. Experimental evaluations on a dataset of 450 FSTS test videos demonstrate the high performance ofFRAVM, with detection accuracy, F1 score, and ROC-AUC achieving 88.64%, 88.58%, and 98.86%, respectively. The ablation analysis confirms that the components inFRAVMare essential for achieving optimal results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".