Offline_SOS_System

Koduru Annarao — Sun, 23 Aug 2026 05:24:26 +0000

Pub.dev Package: Link
GitHub Repository: Link
Imagine getting into a serious car crash in a remote area—a mountain pass, a highway dead zone, or a rural road with zero cell signal.

You open your safety app, or its automated background trigger fires... only to hang indefinitely because it relies on a cloud API to process sensor data or verify the crash.

That single point of failure bugged me for months. Emergency safety features shouldn’t depend on a stable 5G connection. If an engine can detect a crash instantly via onboard physics, our software should be able to do the same on-device.

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

💡 Why Build This?

Most existing Flutter solutions for safety or impact detection suffer from one of three issues:

Cloud Dependency: They stream raw accelerometer data to a backend server for ML inference. (Fails in dead zones).
Simple Threshold Logic: They rely solely on basic G-force > X spikes, leading to massive false-positive rates (like dropping your phone on a table or hitting a pothole).
Heavy Native Dependencies: They require complex, platform-specific iOS/Android native code bindings that are difficult to maintain or integrate into clean Dart architectures.

I wanted a solution that was pure Dart/Flutter at the developer layer, handled complex multi-axis motion patterns via Edge AI, and never made a single network request.

⚙️ How It Works Under the Hood

The package handles the entire pipeline locally on the device:

Continuous Telemetry Buffering: Ingests high-frequency raw data from the device’s accelerometer and gyroscope sensors.
Signal Preprocessing & Feature Extraction: Filters noise, down-samples vector streams, and converts raw hardware readings into structured tensor windows.
On-Device Inference: Runs the preprocessed window through an embedded TensorFlow Lite model using tflite_flutter.
Headless Event Stream: Outputs a clean, reactive stream of crash confidence events—allowing your application logic to decide what happens next (e.g., triggering a local alarm, queuing an offline SMS, or fetching last-known GPS coordinates).

🛠️ Quick Implementation

Here is how simple it is to initialize and listen for crash events in Flutter:


dart
import 'package:offline_sos_system/offline_sos_system.dart';

void main() async {
  WidgetsFlutterBinding.ensureInitialized();

  // Initialize the offline SOS engine
  final sosEngine = OfflineSosSystem();
  await sosEngine.initialize();

  // Listen to real-time crash detection events
  sosEngine.crashStream.listen((CrashEvent event) {
    if (event.isCrashDetected) {
      print('CRASH DETECTED!');
      print('Confidence Score: ${event.confidence}');
      print('Impact Force: ${event.gForce}G');

      // Trigger your app's local emergency protocols here
    }
  });

  // Start monitoring sensor telemetry
  await sosEngine.startMonitoring();
}

DEV Community: Koduru Annarao

Great Post from BHAGYA PRASAD DANNINA!! Check it below!

I was tired of SOS apps failing in dead zones, so I built a pure Dart, 100% offline TFLite crash detection engine.

Offline_SOS_System

💡 Why Build This?

⚙️ How It Works Under the Hood

🛠️ Quick Implementation