Clinical Features-based Lifestyle Quantification Using Mobile Devices for Personalized Augmented Reality Intervention
Bibliographic record
Abstract
We propose a lifestyle quantification method for personalized augmented reality (AR) intervention in everyday life. Maintaining a healthy lifestyle accompanies extensive workforce and costs, because accurate health evaluation and tailored management is a prerequisite. However, research on assessing multiple dimensions of health and timely administration of personalized information has been lacking. To address these issues, first we deduced participant’s physical activity, nutrition, interpersonal relations, and stress management level through their mobile devices. Each participant’s week-long data were filtered by clinical features evidenced in high-level medical literature. Next, based on their health status, we presented personalized AR interventions in their simulated everyday environment. Statistical analysis and user experiment results verified the clinical capability of our method and its effect on health-promoting decision behaviors of participants. Through this feasibility study, we hope to contribute on tailored lifestyle management in our daily lives, for better health of all.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".