KNN-MMD

Article: Zijian Zhao, Zhijie Cai, Tingwei Chen, Xiaoyang Li, Hang Li, Qimei Chen, Guangxu Zhu*, "KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment "(under review, IEEE Transactions on Mobile Computing (TMC))

1. Data

1.1 Dataset

Public Dataset: WiGesture

Proposed Dataset: WiFall (./WiFall)

1.2 Data Preparation

Refer to RS2002/CSI-BERT: Official Repository for The Paper, Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing (github.com)

2. Run the model

To run the model, follow these instructions based on the dataset you are using. For the WiGesture Dataset, use the train.py script, and for the WiFall Dataset, use the train_fall.py script. The steps to execute them are the same, and here we provide an example using train.py.

python train.py --k <shot number> --n <neighbor number for KNN> --p <select the top p samples from testing set for MK-MMD (p<1)> --task <action or people> --lr <learning rate>

Make sure to replace the following placeholders with the appropriate values:

• <shot number>: Specify the shot number.
• <neighbor number for KNN>: Specify the number of neighbors for KNN.
• <select the top p samples from testing set for MK-MMD (p<1)>: Specify the value for p (selecting the top p samples from the testing set for MK-MMD). Note that p should be less than 1.
• <action or people>: Specify the task name as either "action" or "people".
• <learning rate>: Specify the desired learning rate.

Once you have set the appropriate values, run the command in your terminal to start the training process.

3. Reference

@misc{zhao2025knnmmdcrossdomainwireless,
      title={KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment}, 
      author={Zijian Zhao and Zhijie Cai and Tingwei Chen and Xiaoyang Li and Hang Li and Qimei Chen and Guangxu Zhu},
      year={2025},
      eprint={2412.04783},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.04783}, 
}