GeriatricCare 4.0: A Novel 3D Context-Based CareVision Framework for Fall Detection, Fall Classification and Fall Alerts for Elderlies
Bibliographic record
Abstract
Falls can cause severe injuries, if an elderly person has a "long-life."In the presented research work, we have applied the widely known benchmark dataset "L2ei" and the customized "Geriatric-2000" dataset, which contains more than 6,000 elderly fall video sequences.The 25 human skeleton features are extracted using the customized 3D Open Pose methodology.The proposed research work presents a customized 3D Context-based LSTM-CNN enabled CareVision framework for fall position classification for elderlies.Furthermore, the proposed research work is compared with other customized AI-enabled computer vision approaches such as Fine KNN, Medium KNN, Decision Tree, long shortterm memory network (LSTM), Bi-LSTM and Recurrent Neural Network (RNN).The proposed 3D CareVision Framework has achieved an accuracy of 98.23 percent and a ROC value of 0.96.The indicated results demonstrate the efficiency and reliability of the proposed 3D CareVision Framework for elderly fall position classifications.The proposed 3D CareVision Framework will assist elderly personnels in case of emergencies and notify house members by sending emergency fall alerts.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".