Kutluhan Aktar / AI-driven BLE Travel Emergency Assistant
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
This object detection (FOMO) model detects customized keychains (tokens) representing different emergencies:
- Fine
- Danger
- Assist
- Stolen
- Call
After building my object detection model, I deployed my model as a fully optimized and customizable Arduino library and uploaded it to XIAO ESP32S3. Also, I developed an Android application from scratch to obtain the model detection results via BLE and transfer them to a web application in order to inform emergency contacts of the detected emergency class over WhatsApp and SMS via Twilio.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Image training data | NPY file | 60 windows | |
| Image training labels | JSON file | 60 windows | |
| Image testing data | NPY file | 15 windows | |
| Image testing labels | JSON file | 15 windows | |
| Object detection model | TensorFlow Lite (float32) | 82 KB | |
| Object detection model | TensorFlow Lite (int8 quantized) | 55 KB | |
| Object detection model | TensorFlow SavedModel | 186 KB | |
| Object detection model | Keras h5 model | 88 KB |
Clone project
You are viewing a public Edge Impulse project. Clone this project to add data or make changes.
Run this model
Scan QR code or launch in browser
Summary
Data collected
75 itemsProject info
| Project ID | 298397 |
| Project version | 2 |
| License | Apache 2.0 |
