XRF55: A Radio Frequency Dataset for Human Indoor Action Analysis

This repository is a more detailed introduction to XRF55, containing code, hardware tutorials, and instructions for downloading the video dataset. If you have any questions about the above, please submit an issue and we will try to answer them as promptly as possible!

Our project page: https://aiotgroup.github.io/XRF55

If you want to understand or learn how our devices collect data:

Click here for an explanation of how the WiFi, mmWave, RFID, and Kinect devices are initialized, data collected, and processed for the device.

If you want to download our video dataset but have questions about it:

readme_for_video_users.md will help you to process the downloaded XRF55 video dataset correctly!

If you want to reproduce our experiments:

Prerequisites

• Linux
• Python 3.7
• CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Clone this repo:

git clone https://github.com/aiotgroup/XRF55-repo.git
cd XRF55-repo

• Install PyTorch and other dependencies (e.g., torchvision, torch, numpy).
  • For pip users, please type the command pip install -r requirements.txt.
  • For Conda users, you can create a new Conda environment using conda env create -f environment.yml.

XRF train/test

• Download XRF dataset:

  • Download the dataset.zip, unzip it and move it to ./dataset/Raw_dataset/

• Split train/test data: (Used only for split train and test sets, you can rewrite the script to meet different needs)

python split_train_test.py 

• Generate label file:

python generate_txt.py 

• Train a model:

python dml_train.py 

• Test the model:

python dml_eval.py 

File Structrue

.
│  dml_eval.py
│  dml_train.py
│  environment.yaml
│  generate_txt.py
│  opts.py
│  README.md
│  requirements.txt
│  split_train_test.py
│  XRFDataset.py
├─dataset
│  ├─Raw_dataset
│  │  ├─mmWave
│  │  │      XX_XX_XX.npy
│  │  ├─RFID
│  │  │      XX_XX_XX.npy
│  │  └─WiFi
│  │          XX_XX_XX.npy
│  └─XRF_dataset
│      ├─test_data
│      │  ├─mmWave
│      │  │      XX_XX_XX.npy
│      │  ├─RFID
│      │  │      XX_XX_XX.npy
│      │  └─WiFi
│      │          XX_XX_XX.npy
│      └─train_data
│          ├─mmWave
│          │      XX_XX_XX.npy
│          ├─RFID
│          │      XX_XX_XX.npy
│          └─WiFi
│                  XX_XX_XX.npy  
├─model
│      resnet1d.py
│      resnet1d_rfid.py
│      resnet2d.py
├─result
│  ├─conf_matrix
│  ├─learning_curve
│  ├─params
│  └─weights
└─word2vec
        bert_new_sentence_large_uncased.npy

Citations

If you find our works useful in your research, please consider citing:

@article{wang2024xrf55,
  title={XRF55: A Radio Frequency Dataset for Human Indoor Action Analysis},
  author={Wang, Fei and Lv, Yizhe and Zhu, Mengdie and Ding, Han and Han, Jinsong},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  issue={1},
  volume={8},
  year={2024},
  publisher={ACM New York, NY, USA}
}