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!