WiFall

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Dataset Description

Repository: KNN-MMD/WiFall at main · RS2002/KNN-MMD
Paper: KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
Contact: [email protected]
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.

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

@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}, 
}