Create README.md

README.md

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+# WiFall
+
+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:** [[email protected]](mailto:[email protected])  
+- **Collectors:** Zijian Zhao, Tingwei Chen 
+- **Organization:** AI-RAN Lab (hosted by Prof. Guangxu Zhu) in SRIBD, CUHK(SZ)  
+- **Dataset Summary:**
+  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.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance is a `.csv` file representing a 60-second sample with the following columns:  
+
+- **seq**: Row number of the entry.  
+- **timestamp**: UTC+8 time of data collection.  
+- **local_timestamp**: ESP32 local time.  
+- **rssi**: Received Signal Strength Indicator.  
+- **data**: CSI data with 104 numbers representing 52 subcarriers, where each subcarrier's complex CSI value is computed as `a[2i] + a[2i+1]j`.  
+- **Other columns**: Additional ESP32 device information (e.g., MAC, MCS details).
+
+### Data Fields
+
+| Field Name      | Description                                                  |
+| --------------- | ------------------------------------------------------------ |
+| seq             | Row number of the entry                                      |
+| timestamp       | UTC+8 time of data collection                                |
+| local_timestamp | ESP32 local time                                             |
+| rssi            | Received Signal Strength Indicator                           |
+| data            | CSI data (104 numbers, representing 52 subcarriers as complex values) |
+| Other columns   | Additional ESP32 metadata (e.g., MAC address, MCS details)   |
+
+### Data Splits
+
+The dataset is organized by person ID (ID0–ID9), with `.csv` files named after the action performed:  
+
+- **Actions**: fall, jump, sit, stand, walk for 10 individuals (ID0–ID9).
+
+Each directory is structured by person ID, with `.csv` files named after the action performed.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to facilitate research on WiFi-based fall detection, action recognition, and people identification using low-cost ESP32-S3 devices, enabling applications in healthcare, human-computer interaction, and smart environments.
+
+### Source Data
+
+- Initial Data Collection:
+
+  Data was collected in an indoor meeting room with a single transmitter and multiple receivers using ESP32-S3 devices. The setup included:  
+
+  - **Frequency Band:** 2.4 GHz  
+  - **Bandwidth:** 20 MHz (52 subcarriers)  
+  - **Protocol:** 802.11n  
+  - **Waveform:** OFDM  
+  - **Sampling Rate:** ~100 Hz  
+  - **Antenna Configuration:** 1 antenna per device  
+  - **Environment:** Indoor with walls and a soft pad to prevent volunteer injuries.
+
+- **Who are the source data producers?**
+  The data was collected by researchers, with volunteers performing actions in a controlled meeting room environment.
+
+### Annotations
+
+- **Annotation Process:**
+  Each `.csv` file is labeled with the action type (via filename) and person ID (via directory structure). No additional manual annotations were provided.  
+- **Who are the annotators?**
+  The dataset creators labeled the data based on the experimental setup.
+
+### Personal and Sensitive Information
+
+The dataset includes person IDs (ID0–ID9) but does not contain personally identifiable information such as names or biometric data beyond action and CSI patterns.
+
+## Citation
+
+```bibtex
+@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}, 
+}
+```