Offline SOS System: On-Device Crash Detection with Flutter and TensorFlow Lite

Description and use

The developer created the offline_sos_system package for Flutter, using TensorFlow Lite for on-device crash detection without internet connectivity, solving issues with cloud dependency and false positives.

Problem

Existing emergency safety apps fail in remote areas with zero cell signal because they rely on cloud APIs for sensor data processing. Simple threshold logic leads to false positives, and native dependencies complicate maintenance.

Outcome

A pure Dart package that enables offline crash detection using on-device AI, eliminating cloud dependency and reducing false positives compared to simple threshold logic.

Workflow

• Initialize the OfflineSosSystem engine in the Flutter app.
• Start monitoring sensor telemetry (accelerometer and gyroscope).
• The system ingests high-frequency raw data and buffers it.
• Signal preprocessing filters noise and converts readings into structured tensor windows.
• On-device inference runs the tensor windows through an embedded TensorFlow Lite model.
• The system outputs a reactive stream of crash confidence events.
• Application logic listens to the stream and triggers local emergency protocols if a crash is detected.

Evidence

I built and open-sourced offline_sos_system—a pure Dart, 100% offline crash detection engine powered by on-device TensorFlow Lite.

Source

Most existing Flutter solutions... suffer from one of three issues: Cloud Dependency, Simple Threshold Logic, Heavy Native Dependencies.

Source