Create README.md

README.md

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+# WiGesture
+
+The description is generated by Grok3.
+
+## Dataset Description
+
+- **Homepage:** [SSC2025 Competition - Software Defined Network Contest | SDP Competition Platform](http://www.sdp8.net/Dataset?id=5d4ee7ca-d0b0-45e3-9510-abb6e9cdebf9)
+- **Repository:** [CSI-BERT/WiGesture at main · RS2002/CSI-BERT](https://github.com/RS2002/CSI-BERT/tree/main/WiGesture)
+- **Paper**: [Finding the Missing Data: A BERT-Inspired Approach Against Package Loss in Wireless Sensing](https://ieeexplore.ieee.org/document/10620769), IEEE INFOCOM DeepWireless Workshop 2024
+- **Arxiv**: https://arxiv.org/abs/2403.12400
+- **Contact:** [email protected]
+- **Collectors:** Zijian Zhao, Tingwei Chen
+- **Organization:** AI-RAN Lab (hosted by Prof. Guangxu Zhu) in SRIBD, CUHK(SZ)
+- **Dataset Summary:**
+  The WiGesture dataset contains synchronized Channel State Information (CSI), Received Signal Strength Indicator (RSSI), and timestamp data collected using ESP32-S3 devices for WiFi-based human gesture recognition and people identification in a meeting room scenario. The dataset is divided into dynamic gestures (e.g., applause, waving) and static digital gestures (numbers 1–9), performed by eight individuals.
+
+- **Tasks:** Gesture 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 into two main directories:  
+
+- **Dynamic**: Contains dynamic gestures (applause, circleclockwise, frontandafter, leftandright, upanddown, waveright) for 8 individuals (ID1–ID8).  
+- **Static**: Contains static digital gestures (Gesture1–Gesture9, representing numbers 1–9) for 8 individuals (ID1–ID8).
+
+Each directory is structured by person ID, with `.csv` files named after the gesture performed.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to facilitate research on WiFi-based gesture recognition and people identification using low-cost ESP32-S3 devices, enabling applications in 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 gestures in a controlled meeting room environment.
+
+### Annotations
+
+- **Annotation Process:**
+  Each `.csv` file is labeled with the gesture 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 (ID1–ID8) but does not contain personally identifiable information such as names or biometric data beyond gesture and CSI patterns.
+
+## Citation
+
+```bibtex
+@INPROCEEDINGS{10620769,
+  author={Zhao, Zijian and Chen, Tingwei and Meng, Fanyi and Li, Hang and Li, Xiaoyang and Zhu, Guangxu},
+  booktitle={IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)},
+  title={Finding the Missing Data: A BERT-Inspired Approach Against Package Loss in Wireless Sensing},
+  year={2024},
+  volume={},
+  number={},
+  pages={1-6},
+  doi={10.1109/INFOCOMWKSHPS61880.2024.10620769}
+}
+```
+