Identification of Cooking ADL Actions Through Analysis of Thermal Camera Video
Bibliographic record
Abstract
Kitchen use and nutrition are fundamental to well-being and independence for aging adults. Good nutrition enables healthy aging and is necessary for good health, while poor nutrition can lead to health decline. These declines can themselves lead to a reduced meal preparation ability or complexity resulting in continued poor nutrition. Meal preparation is an important Activity of Daily Living (ADL), and this work proposes a method to analyze thermal video for stove top cooking with a method to identify flip events within meal preparation. Flip events are unique to the cooking of some food types and identification allows this cooking behaviour to be assessed. The method applies image processing techniques to the thermal video to identify the food items within the pan and proposes a method to identify flips. A balanced set of cooking recordings that include flip and non-flip events were used and the method provides a sensitivity of 100% and a positive predictive value of 74%. The method can augment kitchen activity of daily living monitoring of cooking habits of aging adults and can assist care givers and clinicians in assessing the changes in the older adult.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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 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".