| --- |
| task_categories: |
| - human-activity-recognition |
| - person-identification |
| - domain-adaptation |
| language: en |
| tags: |
| - wireless-sensing |
| - fall-detection |
| - action-recognition |
| - gesture-recognition |
| - esp32 |
| - csi-data |
| --- |
| |
| # WiFall |
|
|
| [Paper](https://arxiv.org/abs/2412.04783) | [Code](https://github.com/RS2002/KNN-MMD) |
|
|
| The description is generated by Grok3. |
|
|
| ## Dataset Description |
|
|
| - **Contact:** [zzhaock@connect.ust.hk](mailto: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 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. |
|
|
| ## Sample Usage |
|
|
| 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`. |
|
|
| ```bash |
| 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> |
| ``` |
|
|
| Make sure to replace the following placeholders with the appropriate values: |
|
|
| - `<shot number>`: Specify the shot number. |
| - `<neighbor number for KNN>`: Specify the number of neighbors for KNN. |
| - `<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. |
| - `<action or people>`: Specify the task name as either "action" or "people". |
| - `<learning rate>`: Specify the desired learning rate. |
|
|
| Once you have set the appropriate values, run the command in your terminal to start the training process. |
|
|
| ## 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}, |
| } |
| ``` |
