SmartMedBox: A Smart Medicine Box for Visually Impaired People Using IoT and Computer Vision Techniques
Bibliographic record
Abstract
The Internet of Things (IoT) can easily connect real-life objects or physical things to the internet, thus having applications in different domains. Healthcare is one of the prominent application areas. The proposed work aims to design and development of a smart medical box for visually impaired people using IoT and computer vision methods. This application comprises of two modules: first, the QR code scanning module in the mobile app scans the QR code applied to the medicine strip by a pharmacist, it reads the entire medicine information and sets the voice alarms according to a medicine dosage schedule. The second module comprises of the medicine box, an ultrasonic sensor, and an alarm sensor connected to an Arduino microcontroller. When the user cannot find the medicine box, he presses the "locate me" button in the mobile app, and the alarm starts ringing, enabling the user to easily locate the medicine box in the indoor environment on a sound basis. On detection of an object close to the medicine box by an ultrasonic sensor the alarm stops ringing, and that will be the actual location of the medicine box. The experimental analysis of the system with 30 real-time beneficiaries, produces 86.33% accuracy in finding the location of SmartMedBox.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".