Non-Visual and Contactless Wellness Monitoring for Long Term Care Facilities Using mm-Wave Radar Sensors
Why this work is in the frame
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Bibliographic record
Abstract
We propose a radar-based system for wellness monitoring for long-term care (LTC) facilities. Three standalone systems are used to monitor a resident in the washroom, living room and bed. A novel resident detection algorithm is proposed to find the occupied room. Based on the outcome of the algorithm, the resident's washroom frequency, washroom usage time, and location can be recorded. For the resident in the living room area, gait analysis, activity recognition, and vital sign monitoring are performed using sequential deep learning models. Additionally, the sequential deep learning model identifies fall incidents and fall recovery. In the case of a non-recovered fall, an alert is sent to a caregiver or supervisor. The experimental results obtained from a local LTC are highly accurate, demonstrating the effectiveness of radar-based sensors for LTC facilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it