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

This PR improves the dataset card by:
- Adding relevant `task_categories` and `tags` to the metadata for better discoverability.
- Updating the GitHub repository link to point to the root of the repository.
- Including a "Sample Usage" section based on the GitHub README to guide users on how to use the dataset with the associated model.

The existing Arxiv link to the paper has been kept as per instructions.

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