SenseFi: A Benchmark for WiFi CSI Sensing

Introduction

SenseFi is the first open-source benchmark and library for WiFi CSI human sensing, implemented by PyTorch. The state-of-the-art networks, including MLP, CNN, RNN, Transformers, etc, are evaluated on four public datasets across different WiFi CSI platforms. The details are illustrated in our paper Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial.

@article{yang2022benchmark,
  title={Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial},
  author={Yang, Jianfei and Chen, Xinyan and Wang, Dazhuo and Zou, Han and Lu, Chris Xiaoxuan and Sun, Sumei and Xie, Lihua},
  journal={arXiv preprint arXiv:2207.07859},
  year={2022}
}

Requirements

Install pytorch and torchvision (we use pytorch==1.12.0 and torchvision==0.13.0).
pip install -r requirements.txt

Run

Download Processed Data

Please download and organize the processed datasets in this structure:

Benchmark
├── Data
    ├── NTU-Fi_HAR
    │   ├── test_amp
    │   ├── train_amp
    ├── NTU-Fi-HumanID
    │   ├── test_amp
    │   ├── train_amp
    ├── UT_HAR
    │   ├── data
    │   ├── label
    ├── Widardata
    │   ├── test
    │   ├── train

We also offer pre-trained weights for all models

Supervised Learning

To run models with supervised learning (train & test):
Run: python run.py --model [model name] --dataset [dataset name]

You can choose [model name] from the model list below

MLP
LeNet
ResNet18
ResNet50
ResNet101
RNN
GRU
LSTM
BiLSTM
CNN+GRU
ViT

You can choose [dataset name] from the dataset list below

UT_HAR_data
NTU-Fi-HumanID
NTU-Fi_HAR
Widar

Example: python run.py --model ResNet18 --dataset NTU-Fi_HAR

Unsupervised Learning

To run models with unsupervised (self-supervised) learning (train on NTU-Fi HAR & test on NTU-Fi HumanID):
Run: python self_supervised.py --model [model name]

You can choose [model name] from the model list below

MLP
LeNet
ResNet18
ResNet50
ResNet101
RNN
GRU
LSTM
BiLSTM
CNN+GRU
ViT

Example: python self_supervised.py --model MLP
Method: AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning

Model Zoo

MLP

It consists of 3 fully-connected layers followed by activation functions

LeNet

self.encoder : It consists of 3 convolutional layers followed by activation functions and Maxpooling layers to learn features
self.fc : It consists of 2 fully-connected layers followed by activation functions for classification

ResNet

class Bottleneck : Each bottleneck consists of 3 convolutional layers followed by batch normalization operation and activation functions. And adds resudual connection within the bottleneck
class Block : Each block consists of 2 convolutional layers followed by batch normalization operation and activation functions. And adds resudual connection within the block
self.reshape : Reshape the input size into the size of 3 x 32 x 32
self.fc : It consists of a fully-connected layer

RNN

self.rnn : A one-layer RNN structure with a hidden dimension of 64
self.fc : It consists of a fully-connected layer

GRU

self.gru : A one-layer GRU structure with a hidden dimension of 64
self.fc : It consists of a fully-connected layer

LSTM

self.lstm : A one-layer LSTM structure with a hidden dimension of 64
self.fc : It consists of a fully-connected layer

BiLSTM

self.lstm : A one-layer bidirectional LSTM structure with a hidden dimension of 64
self.fc : It consists of a fully-connected layer

CNN+GRU

self.encoder : It consists of 3 convolutional layers followed by activation functions
self.gru : A one-layer GRU structure with a hidden dimension of 64
self.classifier : It consistis a dropout layer followed by a fully-connected layer and an activation function

ViT (Transformers)

class PatchEmbedding : Divide the 2D inputs into small pieces of equal size. Then concatenate each piece with cls_token and do positional encoding operation
class ClassificationHead : It consists of a layer-normalization layer followed by a fully-connected layer
class TransformerEncoderBlock : It consists of multi-head attention block, residual add block and feed forward block. The structure is shown below:

Dataset

UT-HAR

A Survey on Behavior Recognition Using WiFi Channel State Information [Github]

CSI size : 1 x 250 x 90
number of classes : 7
classes : lie down, fall, walk, pickup, run, sit down, stand up
train number : 3977
test number : 996

NTU-HAR

Efficientfi: Towards Large-Scale Lightweight Wifi Sensing via CSI Compression

CSI size : 3 x 114 x 500
number of classes : 6
classes : box, circle, clean, fall, run, walk
train number : 936
test number : 264

NTU-HumanID

CAUTION: A Robust WiFi-based Human Authentication System via Few-shot Open-set Gait Recognition

CSI size : 3 x 114 x 500
number of classes : 14
classes : gaits of 14 subjects
train number : 546
test number : 294

Examples of NTU-Fi data

Widar

Widar3.0: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi [Project]

BVP size : 22 x 20 x 20
number of classes : 22
classes :
Push&Pull, Sweep, Clap, Slide, Draw-N(H), Draw-O(H),Draw-Rectangle(H),
Draw-Triangle(H), Draw-Zigzag(H), Draw-Zigzag(V), Draw-N(V), Draw-O(V), Draw-1,
Draw-2, Draw-3, Draw-4, Draw-5, Draw-6, Draw-7, Draw-8, Draw-9, Draw-10
train number : 34926
test number : 8726

Classes of Widar data

Notice

Please download and unzip all the datasets with Linux system in order to avoid decoding errors.

Datasets Reference

@article{yousefi2017survey,
  title={A survey on behavior recognition using WiFi channel state information},
  author={Yousefi, Siamak and Narui, Hirokazu and Dayal, Sankalp and Ermon, Stefano and Valaee, Shahrokh},
  journal={IEEE Communications Magazine},
  volume={55},
  number={10},
  pages={98--104},
  year={2017},
  publisher={IEEE}
}

@article{yang2022autofi,
  title={AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning},
  author={Yang, Jianfei and Chen, Xinyan and Zou, Han and Wang, Dazhuo and Xie, Lihua},
  journal={arXiv preprint arXiv:2205.01629},
  year={2022}
}

@article{yang2022efficientfi,
  title={Efficientfi: Towards large-scale lightweight wifi sensing via csi compression},
  author={Yang, Jianfei and Chen, Xinyan and Zou, Han and Wang, Dazhuo and Xu, Qianwen and Xie, Lihua},
  journal={IEEE Internet of Things Journal},
  year={2022},
  publisher={IEEE}
}

@article{wang2022caution,
  title={CAUTION: A Robust WiFi-based Human Authentication System via Few-shot Open-set Gait Recognition},
  author={Wang, Dazhuo and Yang, Jianfei and Cui, Wei and Xie, Lihua and Sun, Sumei},
  journal={IEEE Internet of Things Journal},
  year={2022},
  publisher={IEEE}
}

@article{zhang2021widar3,
  title={Widar3. 0: Zero-effort cross-domain gesture recognition with wi-fi},
  author={Zhang, Yi and Zheng, Yue and Qian, Kun and Zhang, Guidong and Liu, Yunhao and Wu, Chenshu and Yang, Zheng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}

852432070/WiFi-CSI-Sensing-Benchmark