Severity of Fall Detection using Digital Twin for Radar Systems
Bibliographic record
Abstract
In long-term care facilities and retirement resi-dences, falls constitute a paramount health challenge for the geriatric demographic. A plurality of technologies have been investigated for use in fall detection. Recently, radar sensors have been proven reliable in detecting falls. However, the problem of detecting the severity of falls has not yet been investigated. Among the major concerns hindering this development is the lack of a representative dataset containing various fall scenarios that can be provided to different machine learning algorithms. Generating this dataset would require having different participants experience various fall scenarios, which would pose a significant risk to their well-being. In this paper, a severity of fall detection algorithm was developed using radar digital-twin generated data. The radar dataset was generated using human kinematic models fed into a full-wave electromagnetic solver. The results indicate an overall 85% accuracy in the detection of fall types. We believe that this is the first of its kind study that demonstrates the ability to use a radar digital twin for the detection and assessment of the severity of falls.
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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 | 0.000 |
| 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 | 0.000 |
| 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".