Few-shot Fall Detection using Shallow Siamese Network
Bibliographic record
Abstract
The threat of falling down is significantly higher for the geriatric population and can lead to serious injuries including death. In the past, classical Machine Learning/Deep Learning-based methods have been successfully investigated for the detection of falls. However, most of these methods require a lot of data in order to be successfully trained for accurate detection. In this work, we propose a shallow architecture using 1 × 1 filters for use in a few-shot Siamese network. The proposed architecture was used in a Siamese network-based fall detection system. The proposed detection system is shown to effectively learn feature representations for the detection of falls when trained with few signals acquired from wearables containing inertial motion unit (SisFall dataset). The proposed system achieved a performance of 93% ± 7% and 72.5% ± 10% in 15 and 1-shot scenarios respectively. Performance comparisons with Siamese convolutional autoencoders and transfer learning¬based approaches demonstrated the superiority of the proposed few shot fall detection system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".