---
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}, 
}
```