Selected work · 11
SAFEBYTE.
Wearable food-safety scanner for dietary and allergen risk detection.
- Goal
- Identify food and flag conflicts with a user’s dietary restrictions.
- My contribution
- Software and systems integration across Pi camera, recognition, lookup, and UI.
- Result
- Functional end-to-end prototype; first place in a 24-hour hackathon.
- Type
- Prototype web product
- Focus
- Camera input · Risk detection · Food safety
- Output
- Wearable concept and app interface
- Link
- Live prototype
Images & documentation
Inside the project.
Constraint & tradeoff
The raw camera stream was unstable and network-heavy under a 24-hour deadline. Keeping preprocessing and recognition close to the Pi limited transfer to the information the browser needed and made the demonstration more reliable.
Recognition pipeline
Camera capture feeds barcode, OCR, or visual recognition according to the information available. Product identity connects to nutrition/allergen data, then a dietary-rule check produces the browser result. I integrated these individual stages into a usable capture-to-feedback workflow.
Demonstrated outcome
The final prototype captured input, identified food through multiple paths, queried nutrition/allergen data, and returned a user-facing result. The project received first place in the hackathon.
Limitations
The demonstrated result is an integrated prototype. Recognition accuracy and allergen coverage were not quantified in the supplied project record, so the page does not claim validated food-safety performance.
Skills
Project record: Engineering portfolio, pp. 10
Explore more of the work.
← Back to Selected Builds