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

Bhagya Prasad Dannina — Sun, 28 Jun 2026 12:07:43 +0000

Hey All,

I’ve been working on a safety application named SafePulse recently and ran into a massive architectural flaw with most existing SOS systems: they rely entirely on cloud servers to analyze telemetry and detect crashes. If an accident happens in a rural area with no network, the app is completely useless.

I wanted a deterministic, zero-latency solution, so I ended up decoupling my crash logic into a standalone, open-source package: offline_sos_system.

How it works: It is a completely headless "Edge AI" engine. It orchestrates raw hardware telemetry (accelerometer _+ _gyroscope), down-samples the vectors, and feeds them directly into an on-device TensorFlow Lite (tflite_flutter) neural network.

Key features:
100% Offline Inference: No network requests, zero latency, maximum privacy.

Strictly Headless: It doesn't handle UI, GPS, or SMS. It just does the complex math and emits a clean Stream, leaving the application layer in full control.

BYOB(Bring Your Own Brain): It bundles a default crash_model.tflite but exposes an injection port if you want to initialize it with your own custom-trained model.

I just pushed version 0.0.5 which hits a 160/160 points on pub.dev. I would love for the community to tear into the architecture, check out the telemetry pipeline, and let me know if there are any optimizations you'd suggest for handling high-frequency sensor streams in Dart!

Pub.dev: https://pub.dev/packages/offline_sos_system

GitHub: https://github.com/bhagyaprasad92/offline_sos_system

Thanks for checking it out!

Hello All! This is my first post.

Bhagya Prasad Dannina — Sun, 28 Jun 2026 11:55:55 +0000

DEV Community: Bhagya Prasad Dannina

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

Hello All! This is my first post.