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---
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},
}
``` |