Low-Cost Smart Insulin Box: A Portable and Interactive System for Enhanced Diabetes Management
Bibliographic record
Abstract
Diabetes is one of the most common life-threatening diseases in the world.The accurate amount, and on-time injection will directly affect the patient's health.The current standard of care for insulin-dependent diabetes management is paper based, so the patients provided with self-management support through health coaches which consumes time and effort.Smart insulin box as proposed meets the needs of the market by integrating electronic technology and IoT network functionality.The interactive box comprises a particular device with embedded sensors in each compartment.Two Infrared and one DHT11 sensors are used.IR sensors are used for detecting the absence or availability of the insulin pen inside the box.While, DHT11 sensor is used for measuring the internal temperature and humidity of the package.The interactive box not only transmits patient status messages to the cloud but also receives a reminder message to patient mobile phone presented by LCD screen.The system manages the insulin injection process and provides a remote monitoring system for doctors using two individual applications.Doctor application provides remote monitoring and controlling for insulin amount and injection time while patient application used to notify patients about injection time, status, and insulin stock status.The system is laboratory tested and evaluated by the authors.The low cost at 84 USD shows promising results.It has a potential impact on patient health outcomes as a smart, low cost, and interactive management system for the insulin injection process which can be integrated with the existing healthcare systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".