mmWave Radar AI Gesture Recognition

Gesture Recognition Using mmWave Sensor - TI AWR1642

This project provides Setup, Record, Train, and Predict functionalities to identify specific gestures using the AWR1642 mmWave radar data. Supported gestures include:

• Swipe Up
• Swipe Down
• Swipe Left
• Swipe Right
• Clockwise Rotation
• Counterclockwise Rotation

The Record feature allows you to capture gestures for training.

Prerequisites

Flash the official demo firmware onto the AWR1642 development board before starting.

• Initial Setup for AWR1642BOOST

Getting Started

• Clone the repository and cd to the project directory

  git clone https://github.com/zyx1121/mmwave-gesture-recognition && cd mmwave-gesture-recognition

• Install dependencies

  pip install -r requirements.txt

• Launch the console to start!

  python console.py

Project Structure

• mmwave.gesture.recognition/
  • models/ - (Trained models)
    • Conv2D.keras
    • LSTM.keras
  • records/ - (Gesture data records)
    • [label]_[%m%d%H%M%S].npy
    • ...
  • console.py - (Main entry point)
  • mmwave.py - (mmWave radar data processing)
  • profile.cfg - (Configuration settings)
  • requirements.txt - (Dependencies)

Command Functions

• cfg : Transmits settings from profile.cfg to the device.

• plot : Plots the radar data in real-time.

• record [gesture] [times] : Records the gesture [gesture] data [times] times and saves it to records/[gesture]_[date].npy.

• train [model] : Trains the model, with a choice of either Conv2D or LSTM.

• predict [model] : Captures real-time radar data and predicts gestures using the selected model (Conv2D or LSTM).

• exit : Exits the console.

License

This project is licensed under the MIT License, which permits commercial use, modification, distribution, and private use.