Hybrid obesity monitoring model using sensors and community engagement
Bibliographic record
Abstract
Obesity has been recognized to be among the principal causes of many chronic diseases such as diabetes, cholesterol, hypertension, and other cardiovascular diseases. Therefore, monitoring, controlling, and preventing obesity will mitigate the risks generated from the complications of these diseases. Comprehensive preventive measures are essential to control the spread of obesity, while healthcare systems should be organized on the basis of locally derived data to provide adequate and affordable care to the increasing groups of overweight and obese people. In this paper, we propose a hybrid model that relies on both data collected from sensors and participatory data collected from a social network community established to provide value-added obesity awareness, monitoring, and prevention. The model encompasses some key smart features including tracking food intake, lifestyle, and exercise activities, generating warnings and recommendations, and triggering interventions whenever needed. Our model also mines the collected data to produce statistical analysis that can be used by health authorities to have a clear picture of the health status of the population and might help in making rational and informed decisions. Moreover, we implement a prototype of our model as a set of Web services using the SOA paradigm and lightweight protocols. Promising results of our prototype are reported and analyzed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".