Introduction | Environment | Dataset | Experiments | Citation | Contact Us

This repository presents WiMANS, including the dataset, source code, and documents. To the best of our knowledge, WiMANS is the first WiFi-based multi-user activity sensing dataset based on WiFi Channel State Information (CSI). WiMANS contains 11286 CSI samples of dual WiFi bands (2.4 / 5 GHz) and synchronized videos for reference and unexplored tasks (e.g., multi-user pose estimation). Each 3-second sample includes 0 to 5 users performing identical/different activities simultaneously, annotated with (anonymized) user identities, locations, and activities.

Sample "act_30_25"	WiFi CSI (5GHz)	Synchronized Video

Sample "act_49_41"	WiFi CSI (2.4GHz)	Synchronized Video

Sample "act_88_30"	WiFi CSI (2.4GHz)	Synchronized Video

• Ubuntu 20.04
• Python 3.9.12
• SciPy 1.7.3
• NumPy 1.21.5
• Pandas 1.4.2
• PyTorch 2.0.1

We suggest to create the environment by installing environment.yaml with Anaconda.

conda env create -f environment.yaml
conda activate WiMANS

Please extract the WiMANS dataset under the dataset directory.

• Step 1: Download the WiMANS dataset from [Kaggle]

• Step 2: Extract the entire WiMANS dataset under the dataset directory

  upzip dataset.zip

• Step 3: Make sure the extracted WiMANS dataset follows such a file structure

  dataset
  | - README.md
  | - annotation.csv        # labels, annotations (e.g., user identities, locations, activities)
  | - wifi_csi
  |   | - mat
  |   |   | - act_1_1.mat   # raw CSI sample labeled "act_1_1"
  |   |   | - act_1_2.mat   # raw CSI sample labeled "act_1_2"
  |   |   | - ...           # totally 11286 raw CSI samples (*.mat files)
  |   | - amp
  |   |   | - act_1_1.npy   # CSI amplitude labeled "act_1_1"
  |   |   | - act_1_2.npy   # CSI amplitude labeled "act_1_2"
  |   |   | - ...           # totally 11286 samples of CSI amplitude (*.npy files)
  | - video
  |   | - act_1_1.mp4       # video sample labeled "act_1_1"
  |   | - act_1_2.mp4       # video sample labeled "act_1_2"
  |   | - ...               # totally 11286 video samples (*.mp4 files)

Annotations are saved in the "annotation.csv" file, which can be read using Pandas.

import pandas as pd
data_pd_y = pd.read_csv(var_path_data_y, dtype = str)
# "var_path_data_y" is the path of "annotation.csv"

Raw CSI data are saved in "*.mat" files, which can be read using SciPy.

import scipy.io as scio
data_mat = scio.loadmat(var_path_mat)
# "var_path_mat" is the path of "*.mat" file

The preprocessed data of CSI amplitude are saved in "*.npy" files, which can be read using NumPy.

import numpy as np
data_csi = np.load(var_path)
# "var_path" is the path of "*.npy" file

Video data are saved in "*.mp4" files, which can be read using PyTorch.

import torchvision
data_video_x, _, _ = torchvision.io.read_video(var_path, output_format = "TCHW")
# "var_path" is the path of "*.mp4" file

The source code of benchmark experiments is under the benchmark directory.

Edit benchmark/wifi_csi/preset.py to select the number(s) of users, WiFi band(s), and environment(s).

preset = {
    ...
    "data": {
        "num_users": ["0", "1", "2", "3", "4", "5"],# e.g., ["0", "1"], ["2", "3", "4", "5"]
        "wifi_band": ["2.4"],                       # e.g., ["2.4"], ["5"], ["2.4", "5"]
        "environment": ["classroom"],               # e.g., ["classroom"], ["meeting_room"], ["empty_room"]
        ...
    },
    ...
}

(Optional) We use benchmark/wifi_csi/preprocess.py to calculate the amplitude of raw WiFi CSI. --dir_mat defines the input directory of raw WiFi CSI. --dir_amp defines the output directory of CSI amplitude. The following code uses the raw WiFi CSI in dataset/wifi_csi/mat to calculate the amplitude and saves the amplitude files to dataset/wifi_csi/amp.

python benchmark/wifi_csi/preprocess.py --dir_mat="dataset/wifi_csi/mat" --dir_amp="dataset/wifi_csi/amp"

Note that this step is optional because we have already provided the preprocessed data in dataset/wifi_csi/amp.

Edit benchmark/wifi_csi/preset.py to set data directory and to tune the hyperparameters, including the learning rate, batch size, etc.

Use benchmark/wifi_csi/run.py to run experiments for WiFi-based models. --model defines the WiFi-based model for experiments. --task defines the human sensing task to evaluate. --repeat defines the number of repeated experiments. If these arguments are not provided, models will load the default settings in benchmark/wifi_csi/preset.py.

• --model: "ST-RF", "MLP", "LSTM", "CNN-1D", "CNN-2D", "CLSTM", "ABLSTM", "THAT"
• --task: "identity", "location", "activity"

The following code repeats experiments 10 times to run the MLP model for WiFi-based human activity recognition.

python benchmark/wifi_csi/run.py --model="MLP" --task="activity" --repeat=10

After experiments, the results will be saved in result.json, which can be modified in benchmark/wifi_csi/preset.py.

Edit benchmark/video/preset.py to select the number(s) of users and environment(s).

preset = {
    ...
    "data": {
        "num_users": ["0", "1", "2", "3", "4", "5"],# e.g., ["0", "1"], ["2", "3", "4", "5"]
        "environment": ["classroom"],               # e.g., ["classroom"], ["meeting_room"], ["empty_room"]
    },
    ...
}

We preprocess video data according to the original papers of video-based models. --path_data_x defines the input directory of raw videos. --path_data_y defines the path of annotation file. --model defines the model for which the videos should be preprocessed. --path_data_pre_x defines the output directory of preprocessed videos (*.npy).

• --model: "ResNet", "S3D", "MViT-v1", "MViT-v2", "Swin-T", "Swin-S"

The following code preprocesses videos in dataset/video for the ResNet model and saves preprocessed files to dataset/cache.

python benchmark/video/preprocess.py --path_data_x="dataset/video" --path_data_y="dataset/annotation.csv" --model="ResNet" --path_data_pre_x="dataset/cache"

Edit benchmark/video/preset.py to set data directory and to tune the hyperparameters, including the learning rate, batch size, etc.

Use benchmark/video/run.py to run experiments for video-based models. --model defines the video-based model for experiments. --task defines the human sensing task to evaluate. --repeat defines the number of repeated experiments. If these arguments are not provided, models will load the default settings in benchmark/video/preset.py.

• --model: "ResNet", "S3D", "MViT-v1", "MViT-v2", "Swin-T", "Swin-S"
• --task: "identity", "location", "activity"

The following code repeats experiments 10 times to run the ResNet model for video-based human activity recognition.

python benchmark/video/run.py --model="ResNet" --task="activity" --repeat=10

After experiments, the results will be saved in result.json, which can be modified in benchmark/video/preset.py.

If you find this repository useful for your research, please consider citing our paper:

@article{huang2024wimans,
  title={WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing},
  author={Huang, Shuokang and Li, Kaihan and You, Di and Chen, Yichong and Lin, Arvin and Liu, Siying and Li, Xiaohui and McCann, Julie A},
  journal={arXiv preprint arXiv:2402.09430},
  year={2024}
}

Please feel free to contact us if you have any questions about WiMANS.

• Shuokang Huang: [email protected]
• Professor Julie A. McCann: [email protected]