Transfer Learning using Computer Vision Models for Fall Detection from UWB Radars
Bibliographic record
Abstract
Detecting when a person falls poses a substantial challenge to researchers because of the risk of serious injuries like femoral neck fractures, brain hemorrhages, or burns, which can lead to significant discomfort and, in some cases, worsen over time, resulting in complications or even fatalities. The effectiveness of fall detection is linked to promptly alerting caregivers, such as nurses, upon detecting a fall. In our study, we present a technique for identifying falls within a 40-square-meter apartment using data collected from three ultra-wideband radars. Our approach integrates pre-trained computer vision models (ResNet, VGG, and AlexNet) for fall detection, which is a binary classification task aimed at distinguishing between fall and non-fall events. To refine the model’s performance, we utilize data representing various fall scenarios simulated by 10 participants across three locations within the apartment. We evaluate the performance of the presented technique by using the leave-one-subject-out strategy. The results consistently demonstrate the superior performance of the ResNet model compared to the VGG and AlexNet models. Notably, our findings indicate an approximate 95% F1 score in fall detection, suggesting promising prospects for real-world deployment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".