Real-time detection and motivation of eating activity in elderly people with dementia using Pose Estimation with TensorFlow and OpenCV.
Why this work is in the frame
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Bibliographic record
Abstract
The objective of this research is to automatically detect the intake of meals for elderly people with dementia living alone by using a Pose Estimation procedure with TensorFlow and OpenCV. Such service based on an Artificial Intelligence product will require minimum intervention of the caregiver or a person as medical support.    We proposed a method for the automatic eating activity detection. We will use this approach for human activity detection in general, for instance monitoring the security and the protection of the patient, automatic motivation of the patient to eat in case no eating detection has been done. The choice of appropriate AI assistive technology was done to satisfy both the elderly people with neurodegenerative disorders and the caregiver, to verify the ethical aspect, simplify design, optimize code, and improve user friendly aspects. 
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| 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