Fall Event Detection System Using Inception-Densenet Inspired Sparse Siamese Network
Bibliographic record
Abstract
A novel few-shot Siamese architecture inspired by Inception and Densenet architectures is proposed as a fall event detection system to detect fall events in signals obtained from waist-worn inertial measurement unit sensors. The proposed system consists of an Inception module followed by a relatively sparse Densenet-based module on each arm of the Siamese network to effectively learn feature representations for the detection of fall events. The proposed system is tested using the SisFall dataset. The proposed system's performance in a few-shot scenario is compared with fall detection systems based on the regular Inception and Densenet121 architectures and the state-of-the-art Siamese convolutional autoencoders. The proposed system outperforms all three fall detection systems. The proposed fall detection system achieved F-scores of 97 ±4% and 68.5 ±10% in 15-shot and 1-shot learning scenarios, respectively.
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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 | 0.000 |
| 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".