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MMWave-radar-human-tracking-and-fall-detection

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Millimeter-Wave Radar-Based Multi-Human Tracking and Fall Detection System

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- config_demo.py
- config_demo_3R.py
- IWR1843_2D.cfg
- IWR1843_3D_10fps_15db.cfg
- IWR1843_3D_10fps_9db.cfg
- IWR1843_3D_20fps_15db.cfg
- IWR1843_3D_20fps_15db_2.cfg
- IWR1843_3D_20fps_9db.cfg
- IWR1843_3D_5fps_9db.cfg
- - mmw_demo_example_script.py
  - parser_mmw_demo.py
- __init__.py
- bgnoise_filter.py
- camera.py
- DBSCAN_generator.py
- email_notifier.py
- frame_early_processor.py
- frame_post_processor.py
- human_object.py
- human_tracking.py
- radar_reader.py
- save_center.py
- sync_monitor.py
- utils.py
- video_compressor.py
- visualizer.py
- data_load.py
- draw_path.py
- png_to_mp4.py
- rawdata_load.py
.gitignore
LICENSE
main.py
README.md
requirements.txt
Sys_flowchart.jpg

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# Millimeter-Wave Radar-Based Multi-Human Tracking and Fall Detection System

## Overview
This repository contains the implementation and code resources for our paper: **Advanced Millimeter-Wave Radar System for Real-Time Multiple-Human Tracking and Fall Detection** (link: https://doi.org/10.3390/s24113660). The study explores an indoor system that employs Millimeter-Wave radars to track multiple humans and detect falls in real time. By integrating signals from non-intrusive radars, our framework addresses challenges such as mobility inconvenience, lighting conditions, and privacy issues inherent in wearable or camera-based systems.

### Key Features
- **Multi-Human Tracking**: Tracks multiple humans simultaneously with high precision.
- **Real-Time Fall Detection**: Accurately predicts and classifies human body statuses, including falls.
- **Advanced Signal Processing**: Employs Dynamic DBSCAN clustering and innovative feedback loops for enhanced accuracy.
- **Privacy and Accessibility**: Operates without cameras or wearables, ensuring non-intrusive monitoring. Camera module in the project is just for ground truth.

---

## Table of Contents
1. [System Architecture](#system-architecture)
2. [Installation and Usage](#Installation-and-Usage)
3. [License](#license)

---

## System Architecture
### Components
- **Radar Hardware**: 3 **Millimeter-Wave radars** from Texas Instruments.
- **Real-Time Framework**: Integrates radar signals to track and classify human activity.

### Workflow
![System Flowchart Diagram](https://raw.githubusercontent.com/DarkSZChao/MMWave-radar-human-tracking-and-fall-detection/main/Sys_flowchart.jpg)

---

## Installation and Usage
### Prerequisites
- Python 3.8 or higher
- Libraries: `numpy`, `Send2Trash`,`scipy`,`pyserial`,`matplotlib`,`scikit-learn`,`opencv-python`,`google-api-python-client`,`google-auth-oauthlib`,`func-timeout`,`moviepy`

### Steps
1. Clone this repository:
   ```bash
   git clone https://github.com/DarkSZChao/MMWave_Radar_Human_Tracking_and_Fall_detection.git
   cd MMWave_Radar_Human_Tracking_and_Fall_detection

2. Install dependencies:
   ```bash
   pip install -r requirements.txt

3. Connect radars.

4. Check which port number the radars are using:

5. Config the parameters:
   ```bash
   cd cfg
   ```
   open **config_demo.py**, under **RADAR_CFG_LIST** parameter, update **cfg_port_name** and **data_port_name** for radars

6. Go back to root folder and start the system by runing **main.py**

---

## License
This project is licensed under the MIT License.

You are free to use, modify, and distribute this project, provided that you include the original copyright and license notice in any copy of the project or substantial portions of it.

See the [LICENSE](LICENSE) file for more details.