Kutluhan Aktar / AI-driven BLE Travel Emergency Assistant Public

Kutluhan Aktar / AI-driven BLE Travel Emergency Assistant

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Object detection
3D printing BLE assistive device XIAO ESP32S3 Android web WhatsApp SMS Twilio

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.

home_8.jpg

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

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Summary

Data collected
75 items

Project info

Project ID 298397
Project version 2
License Apache 2.0