System of sign language recognition for smart home application

About The Project

Read LabMaking_Project_Report.pdf and Lab di making_Project_Slides.pdf.

Built With

• Edge Impulse
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Hardware

• Arduino Tiny Machine Learning Kit
  • 1 Arduino Nano 33 BLE Sense board
  • 1 OV7675 Camera
  • 1 Arduino Tiny Machine Learning Shield
  • 1 USB A to Micro USB Cable

Before starting

• TensorFlow Lite for Microcontrollers
• Arduino docs: Edge Impulse with the Nano 33 BLE Sense
• Edge Impulse docs: Arduino Nano 33 BLE Sense

Getting Started

Go to Edge Impulse project to modify the model.

Inference

Edge Impulse CLI command

Here are the steps required to to use the Edge Impulse CLI command:

1. Download sign-detection-nano-33-ble-sense

2. Unzip the file to a location of your choosing and make this directory your current directory

3. Press RESET twice on the Nano to start the bootloader. Orange LED should be flashing

4. Execute the flash script for your operating system from the command prompt. For example, on a Linux system:

    $./flash_linux.sh

   If this is successful, press RESET once on the Nano to enter normal mode.

5. From a command prompt run the Edge Impulse CLI command

    edge-impulse-run-impulse –-debug

   This will start a inferencing session on the Nano 33 BLE and the results will print out on the command prompt screen. If you want to see a feed of the camera and live classification in your browser, use the address shown on the command prompt screen (typically 192.168.12.49:4915).

Use

Arduino IDE

Upload the sign-detection-sketch on Arduino.

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