An Analytical Threshold-based Classification Technique for Post-incident Fall Detection
Bibliographic record
Abstract
Rapid growth in the older adult population has increased demand for aging supports. The shortfall in healthcare worker availability has created a demand for alternate modes of support, such as technology-based interventions. Falls are recognized as the most substantial health risk associated with older individuals, presenting a significant obstacle for aging in place safely. Autonomous fall detection mechanisms serve as an alternative means of support, improving safety within the home for older adults. This study proposes a novel approach utilizing post-fall incident images, in combination with joint position geometric quantification and statistical thresholding to detect the fallen state of a person. This technique’s classification ability rivals that of machine learning techniques, offering fall-detection independent of a learning model. It is intended to serve as a response mechanism operating independently of real-time fall data, or incident viewing. This technique allows for integration of a response mechanism in stationary home devices, or mobile robotic devices, with image capture capabilities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".