Recognizing Fall Risk Factors with Convolutional Neural Network
Bibliographic record
Abstract
As the number of elderly living alone is increasing every year, some seemingly common daily activities can potentially raise the risk of serious injuries and fatal accidents for these elderly. While falls can occur anywhere, they most often occur at home, this is especially true among the elderly. Without timely notification to medical personnel and assistance, the resulting injuries could be life-threatening. As falls are caused by many different risk factors, it is necessary to identify potential incidents and make needed changes accordingly in order to reduce the risk and prevent falls. Therefore, we propose a system using surveillance cameras to detect daily activities (e.g., bending down, sitting, standing, and walking) that potentially increase the risk of falling. Moreover, we recognize high risk factors of falls such as ones that involve using the phone while performing an activity, not paying attention to obstacles, and not holding the handrails while going upstairs or downstairs. Convolutional neural network is applied for activity classification in this work. This warning system is utilized for detecting risk factors of falls that commonly occur among the elderly, which could then be used to trigger a message and/or audible alert to designated persons such as a doctor, a caregiver, or family members for timely assistance and care.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".