Integrating Real-Time Health Status into Everyday Objects: A Design Case Study on Enhancing Diabetic Health Monitoring with Artistic Creations
Bibliographic record
Abstract
How can everyday objects interact or change based on a person’s real-time health status? In the following design case study, we bring together the fields of health and technology with art in the context of diabetic health monitoring. Specifically, we combine smart sock wearable diabetic monitoring technology with home decor (dynamic artwork) as well as artistically designed changing smartwatch faces. The aim is to allow for health awareness for both diabetic patients and their caregivers/relatives in an ambient living context. By creating subtly changing artworks as well as changing smartwatch faces that are based on health status, we can allow for gentle non-emergent notifications to the diabetic and their caregivers/relatives to create health status awareness in order to positively impact the quality of their health and home-life experience.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".