This is the PyTorch source code for the WiFi CSI-based activity recognition. The code runs on Python 3. Install the dependencies and prepare the datasets with the following commands:

The two public datasets used in the paper are shown below.

The data that we extract from raw CSI data for our experiments can be downloaded from Baidu Netdisk or Google Drive:

Data of CSI amplitudes: Data_CsiAmplitudeCut Baidu Netdisk: https://pan.baidu.com/s/12DwlT58PzlVAyBc-lYx1lw (Password: k8yp) or Google Drive: https://drive.google.com/drive/folders/1PLzV6ZWAauMQLf08NUkd5UeKrqyGMHgv

Manually marked Labels for CSI amplitude data: Label_CsiAmplitudeCut Baidu: https://pan.baidu.com/s/1nY5Og4NlLb7VH5oBQ-LH9w (Password: xnra) or Google: https://drive.google.com/drive/folders/1855zX-93QjmAt2wSeJk0rTJRiPaFMGBd (1 boxing; 2 hand swing; 3 picking up; 4 hand raising; 5 running; 6 pushing; 7 squatting; 8 drawing O; 9 walking; 10 drawing X)

Also the raw CSI data we collected can be downloaded via Baidu or Google: Data_RawCSIDat. Note that there is no need to download the raw CSI data for running our experiments. Downloading Data_CsiAmplitudeCut and Label_CsiAmplitudeCut is enough for our experiments. Baidu: https://pan.baidu.com/s/1FpA2u_fzFIh4FuNIcWOPdQ (Password: hhcv) or Google: https://drive.google.com/drive/folders/1vUeJYChsDgBzv7bJbiKDEfAHQje3SW9G

The SignFi dataset comes from the link below: https://github.com/yongsen/SignFi

Python 3.7

Tensorflow 2.4.1

The codes are tested under window10.

01DataProcessing: This is used to extract the data in CSI format from the original WiFi and convert it into PNG format in order to make better use of the data.

02DataGenerator: This is used to generate augmented samples based on the source data.

03ActivityRecognition: This is used to conduct activity recognition.