OraScan H - Halitosis IoT Device
A pocket-sized consumer device for halitosis detection with BLE and on-device AI.
Software Completion
BLE Scan-to-Result
Gas Sensor Arrays
Tech Stack
The problem
Bad breath affects 1 in 4 people, but most never get it properly diagnosed because it's embarrassing to bring up with a doctor. People need a way to check privately at home - without buying expensive clinical lab equipment or visiting a specialist.
The challenge
Halitosis affects an estimated 25% of the population but is severely under-diagnosed due to social stigma around clinical evaluation. The product needed to be a consumer-grade, handheld device that anyone could use at home - not a lab instrument. The engineering challenges were multi-layered: H2S sensor signal is noisy and temperature-sensitive, requiring careful analog conditioning; the Raspberry Pi Zero 2W has only 1 GB RAM, constraining the on-device AI pipeline; the Flutter mobile app needed to reliably pair via BLE, stream sensor readings, and present results in a clinically actionable way.
Architecture & System Design
IoT device with dual gas sensor arrays connected to mobile app via wireless protocol. On-device machine learning model processes sensor readings for analysis. Mobile app provides real-time results and trend history. Backend service handles data persistence and optional cloud synchronization with secure firmware update mechanism.
The hardware stack centres on a Raspberry Pi Zero 2W running a Python BLE GATT server (D-Bus/BlueZ). Two gas sensor arrays (DTS4H2S + MQ316) are read via ADS1115 ADC over I2C, with GPIO-based LED status indicators and a serial interface for auxiliary sensors. Sensor readings feed into a TensorFlow Lite model running on-device that classifies halitosis severity. The Flutter mobile app (flutter_blue_plus) discovers and pairs with the device, streams live readings, and renders severity scores with trend history. A PHP 8.2 + MySQL 8.0 backend on Hostinger handles session persistence and optional cloud sync. OTA firmware updates are signed and verified on-device before installation.
Results
OraScan H reached 92% software completion in Sprint 3 with production-ready mobile app and backend. BLE pairing achieves stable connection within 3 seconds. The TFLite classifier runs on the Pi Zero 2W in under 500ms. The Flutter app is live on both iOS and Android with a full onboarding, pairing, and results flow. Hostinger backend handles session persistence with rate-limited API endpoints and MIME-validated image uploads.
Gallery & Demos
Doctors Dashboard - Session Ledger
Every recorded session with model output, reference assessment and site, keyed by pseudonymous participant code.
Doctors Dashboard - Case Record
One participant's captures, sensor readings and clinical figures.
Mobile App - Device Pairing
BLE pairing with the handheld, stable in under 3 seconds.
Mobile App - Guided Capture
On-screen guidance for the breath sample and smile image.
Mobile App - Assessment Result
The classifier result and next steps shown to the user.
Device Front View
Handheld form factor with mouthpiece intake, dual gas sensor arrays inside, and GPIO-driven LED status indicators.
LED Status States
Four GPIO-driven LED states: off (standby), green (scan complete / pass), blue (scanning), red (alert / fail).
Hardware Evolution
Design iterations from prototype to production across multiple sprints.
Cloud Architecture
BLE pairing flow, on-device TFLite inference, and PHP backend session storage with rate-limited endpoints.
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Interested in this work?
I can walk through the architecture and code during an interview.