Improve dataset card: Add task categories, root GitHub link, and sample usage

README.md

@@ -1,11 +1,26 @@
+---
+task_categories:
+  - human-activity-recognition
+  - person-identification
+  - domain-adaptation
+language: en
+tags:
+  - wireless-sensing
+  - fall-detection
+  - action-recognition
+  - gesture-recognition
+  - esp32
+  - csi-data
+---
+
 # WiFall
 
+[Paper](https://arxiv.org/abs/2412.04783) | [Code](https://github.com/RS2002/KNN-MMD)
+
 The description is generated by Grok3.
 
 ## Dataset Description
 
-- **Repository:** [KNN-MMD/WiFall at main · RS2002/KNN-MMD](https://github.com/RS2002/KNN-MMD/tree/main/WiFall)
-- **Paper:** [KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment](https://arxiv.org/abs/2412.04783)
 - **Contact:** [zzhaock@connect.ust.hk](mailto:zzhaock@connect.ust.hk)  
 - **Collectors:** Zijian Zhao, Tingwei Chen 
 - **Organization:** AI-RAN Lab (hosted by Prof. Guangxu Zhu) in SRIBD, CUHK(SZ)  
@@ -13,6 +28,24 @@ The description is generated by Grok3.
   The WiFall dataset contains synchronized Channel State Information (CSI), Received Signal Strength Indicator (RSSI), and timestamp data collected using ESP32-S3 devices for WiFi-based fall detection, action recognition, and people identification in a meeting room scenario. The dataset includes actions (fall, jump, sit, stand, walk) performed by ten individuals.
 - **Tasks:** Fall Detection, Action Recognition, People Identification, Cross-Domain Tasks.
 
+## Sample Usage
+
+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`.
+
+```bash
+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.
+
 ## Dataset Structure
 
 ### Data Instances