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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.

(wait for Arxiv)

Requirements

  1. Install pytorch and torchvision (we use pytorch==1.12.0 and torchvision==0.13.0).
  2. 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

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

Self-supervised Learning

To run models with self-supervised learning (train & test):
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
AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning

Model

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

  • 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

NTU-HAR

NTU-HumanID

Examples of NTU-Fi data

Widar

  • 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

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

Datasets Reference

@ARTICLE{8067693,  author={Yousefi, Siamak and Narui, Hirokazu and Dayal, Sankalp and Ermon, Stefano and Valaee, Shahrokh},  journal={IEEE Communications Magazine},   title={A Survey on Behavior Recognition Using WiFi Channel State Information},   year={2017},  volume={55},  number={10},  pages={98-104},  doi={10.1109/MCOM.2017.1700082}}