A Deep Learning-Based Multi-Feature Fall Detection Approach Using Silhouette and Motion Data
Bibliographic record
Abstract
Falls constitute a major public health concern, particularly among the elderly, often resulting in severe injuries or fatalities. Traditional fall detection methods relying on manual observation or conventional image analysis lack accuracy and responsiveness in dynamic environments. To address these limitations, we propose a deep learning-based framework integrating silhouette-based spatial analysis, motion tracking, and joint angle computation for robust fall detection. The approach utilizes pretrained neural networks—Segment Anything Model-2 (SAM2), MediaPipe Pose, and ResNet-18—to extract human body features and classify postures as fall or non-fall. By focusing on structural and temporal cues rather than raw RGB data, the method enhances robustness against lighting variations and background clutter. Additionally, an optimized frame sampling strategy ensures computational efficiency, enabling real-time detection with high accuracy. Experimental results demonstrate the model’s effectiveness for applications in healthcare, elderly monitoring, and surveillance systems, where rapid intervention is critical.
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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 | 0.001 |
| 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) | 0.002 | 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".