| # 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:** zzhaock@connect.ust.hk | |
| - **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 | |
| **Notice:** We apologize that due to a device error, we missed the waiver right of user5 in the dynamic part of the dataset. | |
| ### 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} | |
| } | |
| ``` | |