Dual-Module Vision-Based Framework for Close-Proximity Real-Time Fall Detection
Bibliographic record
Abstract
Falls pose a serious threat to older adults' independence and well-being, with sit-to-stand (STS) transition frequently associated with fall incidents. To address this challenge, we propose a real-time fall detection integrated into a mobile assistive robot SkyWalker. Our approach utilizes a depth camera with onboard processing capabilities positioned at close proximity (approximately 0.5 m). A 3D skeletal model derived from MediaPipe tracks the user's motion in real-time, extracting 14 key kinematic features that capture biomechanical information. These features serve as input to a dual-modular classification framework based on support vector machines (SVMs): one classifier predicts STS phases (sitting, rising, switching, standing). while the other identifies irregular motions indicative of falls. By focusing on this reduced yet discriminative feature set, our system remains both computationally efficient and robust to skeleton distortions often encountered at close range. We evaluated our approach using separate datasets for phase classification and fall detection, achieving high accuracy in real-time classification. Future work will extend the system to enable proactive fall prevention strategies, ensuring safer STS transitions for older adults.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".