Skip to content
/ ARIL Public

Codes of paper: Joint Activity Recognition and Indoor Localization with WiFi Fingerprints

Notifications You must be signed in to change notification settings

geekfeiw/ARIL

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

18 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Action Recognition and Indoor Localization

Code and Data of the paper, Joint Activity Recognition and Indoor Localization with WiFi Fingerprints.

Evaluated Environment

  1. PyTorch 1.0.0

Usage

  1. Please download data, and decompress it at the root folder of this repository.

Activity Label: 0. hand up; 1. hand down; 2. hand left; 3. hand right; 4. hand circle; 5. hand cross. Location Label: 0, 1, 2, ..., 15

  1. Please download pre-trained weights, and decompress it at the root folder of this repository.

  2. Then run train.py or test.py

You may need original data (not segmented and upsampled) for your research, here

Hardware: Ettus N210 and Ettus Clock

hardware

1D CNN

  1. 1D residual block

  1. 1D ResNet-[1,1,1,1]

For t-SNE visualization

Please download vis, and run main_plot_tsne.m

  1. t-SNE visualization for activity recognition tsne_act

  2. t-SNE visualization for indoor localization tsne_loc

If this helps your research, please cite this paper.

@article{wang2019joint,
  title={Joint Activity Recognition and Indoor Localization With WiFi Fingerprints},
  author={Wang, Fei and Feng, Jianwei and Zhao, Yinliang and Zhang, Xiaobin and Zhang, Shiyuan and Han, Jinsong},
  journal={IEEE Access},
  volume={7},
  pages={80058--80068},
  year={2019},
}

About

Codes of paper: Joint Activity Recognition and Indoor Localization with WiFi Fingerprints

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published