DrowsyGuard: An Offline Drowsiness Alert for Ride-Hailing & Truck Drivers

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

DrowsyGuard is a React Native app that watches the driver's face through the phone's front camera and sounds an alarm when it detects drowsiness: the phone vibrates, says "Wake up! Pull over and rest," and the screen turns red.

Who would pay: [ride-hailing and delivery drivers, long-haul truckers, small logistics fleets and bus operators]. Fatigue is a real safety and cost problem for them, and dedicated in-cab monitoring hardware is expensive. A phone they already own is a far cheaper option.

How it decides:

• Every frame, it computes the eye aspect ratio (EAR) from the face landmarks.
• It raises the alarm if the eyes stay closed for about 1.5 seconds, or are closed for more than 30% of the last 30 seconds (PERCLOS, a standard fatigue measure).
• It also warns the driver if no face is visible for 3 seconds, meaning they are not looking at the road.

Demo

Code

https://github.com/dimaswahyu-official/drownsy-app-ai.git

How I Built It

• Open-weight model: MediaPipe Face Landmarker's face_landmarks_detector.tflite (Apache-2.0). It outputs 478 3D face landmarks per frame.
• Inference: runs fully on the phone through react-native-fast-tflite, called from a Vision Camera frame processor at about 15 fps.
• App: React Native (Expo dev build) with react-native-vision-camera v4, vision-camera-resize-plugin and react-native-worklets-core.
• Detection logic: a small, model-independent TypeScript class (drowsiness.ts) that computes EAR from six landmarks per eye and applies the eyes-closed duration and PERCLOS rules. Swapping the model doesn't touch the alert logic.
• Alerts: vibration plus text-to-speech (expo-speech), with the screen kept awake.
• Calibration: [describe what you measured, e.g. your EAR for open vs closed eyes, and the threshold you picked].

Why Does Open Innovation Matter?

• Privacy: the driver's face video never leaves the phone. No uploads, no accounts. Drivers and employers don't have to trust a server with facial video.
• Works offline: it works in tunnels, on rural highways and in areas with poor signal, which is exactly where drowsy driving is most dangerous. A cloud API would fail there.
• Zero running cost: there are no per-frame API fees. A closed vision API called about 15 times a second would make a cheap driver app unaffordable. With an open model, the marginal cost per user is zero.
• Tunable and swappable: I can adjust thresholds, retrain, or replace the model for real conditions such as night driving, glasses and helmets, without waiting for a vendor. [Add anything you actually tuned.]
• No lock-in: no vendor can change the pricing or shut off the API my business depends on.