Wallhack1.8k Dataset | Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition

This repository contains the Wallhack1.8k dataset for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the CAD models (of 3D-printable parts) of the WiFi systems proposed in [2].

PyTroch Dataloader

A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: https://github.com/StrohmayerJ/wallhack1.8k

Dataset Description

The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).

To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:

• LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system
• LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system
• NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system
• NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system

These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector H being stored in the "data" field and the class label being stored in the "class" field. H is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of H (e.g., via numpy.abs(H)) yields the subcarrier amplitudes A.

To extract the 52 L-LTF subcarriers used in [1], the following indices of A are to be selected:

# 52 L-LTF subcarriers 
csi_valid_subcarrier_index = []
csi_valid_subcarrier_index += [i for i in range(6, 32)]
csi_valid_subcarrier_index += [i for i in range(33, 59)]

Additional 56 HT-LTF subcarriers can be selected via:

# 56 HT-LTF subcarriers
csi_valid_subcarrier_index += [i for i in range(66, 94)]     
csi_valid_subcarrier_index += [i for i in range(95, 123)]

For more details on subcarrier selection, see ESP-IDF (Section Wi-Fi Channel State Information) and esp-csi.

Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the spectrograms/ directory.

The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label]

• Spectrogram index: [0, ..., n]
• Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."
• Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.

Dataset Overview:

Table 1: Raw WiFi packet sequences.

Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k.

Download and Use
This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2].

[1] Strohmayer, Julian, and Martin Kampel. "Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition." IFIP International Conference on Artificial Intelligence Applications and Innovations. Cham: Springer Nature Switzerland, 2024.

[2] Strohmayer, Julian, and Martin Kampel. "Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition." arXiv preprint arXiv:2401.01388 (2024).

BibTeX citations:

@inproceedings{strohmayer2024data,
  title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition},
  author={Strohmayer, Julian and Kampel, Martin},
  booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations},
  pages={42--56},
  year={2024},
  organization={Springer}}

@INPROCEEDINGS{strohmayer10647666,
  author={Strohmayer, Julian and Kampel, Martin},
  booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, 
  title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition}, 
  year={2024},
  volume={},
  number={},
  pages={3594-3599},
  doi={10.1109/ICIP51287.2024.10647666}}