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--- |
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task_categories: |
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- human-activity-recognition |
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- person-identification |
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- domain-adaptation |
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language: en |
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tags: |
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- wireless-sensing |
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- fall-detection |
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- action-recognition |
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- gesture-recognition |
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- esp32 |
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- csi-data |
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--- |
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# WiFall |
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[Paper](https://arxiv.org/abs/2412.04783) | [Code](https://github.com/RS2002/KNN-MMD) |
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The description is generated by Grok3. |
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## Dataset Description |
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- **Contact:** [zzhaock@connect.ust.hk](mailto:zzhaock@connect.ust.hk) |
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- **Collectors:** Zijian Zhao, Tingwei Chen |
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- **Organization:** AI-RAN Lab (hosted by Prof. Guangxu Zhu) in SRIBD, CUHK(SZ) |
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- **Dataset Summary:** |
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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. |
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- **Tasks:** Fall Detection, Action Recognition, People Identification, Cross-Domain Tasks. |
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## Sample Usage |
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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`. |
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```bash |
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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> |
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``` |
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Make sure to replace the following placeholders with the appropriate values: |
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- `<shot number>`: Specify the shot number. |
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- `<neighbor number for KNN>`: Specify the number of neighbors for KNN. |
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- `<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. |
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- `<action or people>`: Specify the task name as either "action" or "people". |
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- `<learning rate>`: Specify the desired learning rate. |
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Once you have set the appropriate values, run the command in your terminal to start the training process. |
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## Dataset Structure |
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### Data Instances |
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Each instance is a `.csv` file representing a 60-second sample with the following columns: |
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- **seq**: Row number of the entry. |
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- **timestamp**: UTC+8 time of data collection. |
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- **local_timestamp**: ESP32 local time. |
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- **rssi**: Received Signal Strength Indicator. |
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- **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`. |
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- **Other columns**: Additional ESP32 device information (e.g., MAC, MCS details). |
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### Data Fields |
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| Field Name | Description | |
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| --------------- | ------------------------------------------------------------ | |
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| seq | Row number of the entry | |
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| timestamp | UTC+8 time of data collection | |
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| local_timestamp | ESP32 local time | |
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| rssi | Received Signal Strength Indicator | |
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| data | CSI data (104 numbers, representing 52 subcarriers as complex values) | |
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| Other columns | Additional ESP32 metadata (e.g., MAC address, MCS details) | |
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### Data Splits |
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The dataset is organized by person ID (ID0–ID9), with `.csv` files named after the action performed: |
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- **Actions**: fall, jump, sit, stand, walk for 10 individuals (ID0–ID9). |
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Each directory is structured by person ID, with `.csv` files named after the action performed. |
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## Dataset Creation |
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### Curation Rationale |
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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. |
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### Source Data |
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- Initial Data Collection: |
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Data was collected in an indoor meeting room with a single transmitter and multiple receivers using ESP32-S3 devices. The setup included: |
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- **Frequency Band:** 2.4 GHz |
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- **Bandwidth:** 20 MHz (52 subcarriers) |
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- **Protocol:** 802.11n |
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- **Waveform:** OFDM |
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- **Sampling Rate:** ~100 Hz |
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- **Antenna Configuration:** 1 antenna per device |
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- **Environment:** Indoor with walls and a soft pad to prevent volunteer injuries. |
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- **Who are the source data producers?** |
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The data was collected by researchers, with volunteers performing actions in a controlled meeting room environment. |
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### Annotations |
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- **Annotation Process:** |
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Each `.csv` file is labeled with the action type (via filename) and person ID (via directory structure). No additional manual annotations were provided. |
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- **Who are the annotators?** |
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The dataset creators labeled the data based on the experimental setup. |
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### Personal and Sensitive Information |
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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. |
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## Citation |
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```bibtex |
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@misc{zhao2025knnmmdcrossdomainwireless, |
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title={KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment}, |
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author={Zijian Zhao and Zhijie Cai and Tingwei Chen and Xiaoyang Li and Hang Li and Qimei Chen and Guangxu Zhu}, |
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year={2025}, |
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eprint={2412.04783}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2412.04783}, |
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} |
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``` |
