Radar-Based Timed Up And Go Test For Multiperson Providing A Risk Of Falling Assessment
Bibliographic record
Abstract
Abstract Objective: Over the past decade, the research community has made major advances in technologies for detecting risk of falling in elderly, which led to assistive systems that can help to improve physical strength and limit isolation. However, it is still expensive to extend these systems to a multi-user performing a clinical test, such as the Timed Up and Go (TUG). In this paper, we therefore propose a new avenue, a radar-based system that transcends these limitations. Results: The proposed methodology is evaluated using data from real-world scenarios. It is found that distinguishing two participants performing a TUG test at the same time is more accurate when they are walking in opposite direction compared to perfectly synchronized walking in the same direction. However, although it was more challenging to distinguish two participants in this latter scenario, our results show that when they are offset by a certain distance, the proposed algorithm can track them separately. Our goal was therefore to relieve congestion in medical settings by investigating the feasibility of administering a clinical test with more than one participant at the same time thanks to an UWB radar sensor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".