Bibliographic record
Abstract
Falls are a growing issue in society and has become a hot topic in the healthcare domain. Falls are more likely to occur to due to age or health problems such as cardiovascular issues and muscle weakness. In this work we focus on fall detection. The aftereffects of falls often lead to the use of prescription pain medications. We are motivated to help prevent suicide attempts by overdose in the Canadian correctional services. Most previous studies were based on hand-crafted features which limit the robustness and generality of the system. We therefore propose a general vision-based system, using Spatial Temporal Graph Convolutional Networks (ST-GCN). This system has proven its efficiency and robustness in the action recognition domain. Contrary to previous works, this model can be applied directly to new data without the need to retrain the model while offering good accuracy. Additionally, with the help of transfer learning we can solve the insufficient data problem. By using three public datasets: the NTU RGB-D dataset, the TST Fall detection dataset v2 and the Fallfree dataset to validate our method, we achieved a 100% accuracy, surpassing the state-of-the-art.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".