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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:** 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}
}
```
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