# Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition

arXiv: https://arxiv.org/abs/2401.00964 · HTML: https://mlsanity.com/paper/2401.00964/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 1 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning](https://mlsanity.com/paper/1906.08171/) (2019)
- [Simple and Effective Augmentation Methods for CSI Based Indoor Localization](https://mlsanity.com/paper/2211.10790/) (2022)
- [DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization](https://mlsanity.com/paper/2101.10833/) (2021)

## Built on by (most cited first, 5 of 5)

- [Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data](https://mlsanity.com/paper/2506.23174/) (2025)
- [CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing](https://mlsanity.com/paper/2512.04723/) (2025)
- [A Sensing Dataset Protocol for Benchmarking and Multi-Task Wireless Sensing](https://mlsanity.com/paper/2512.12180/) (2025)
- [Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data](https://mlsanity.com/paper/2603.11858/) (2026)
- [MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2605.01369/) (2026)

## Abstract (arXiv)

The recognition of human activities based on WiFi Channel State Information (CSI) enables contactless and visual privacy-preserving sensing in indoor environments. However, poor model generalization, due to varying environmental conditions and sensing hardware, is a well-known problem in this space. To address this issue, in this work, data augmentation techniques commonly used in image-based learning are applied to WiFi CSI to investigate their effects on model generalization performance in cross-scenario and cross-system settings. In particular, we focus on the generalization between line-of-sight (LOS) and non-line-of-sight (NLOS) through-wall scenarios, as well as on the generalization between different antenna systems, which remains under-explored. We collect and make publicly available a dataset of CSI amplitude spectrograms of human activities. Utilizing this data, an ablation study is conducted in which activity recognition models based on the EfficientNetV2 architecture are trained, allowing us to assess the effects of each augmentation on model generalization performance. The gathered results show that specific combinations of simple data augmentation techniques applied to CSI amplitude data can significantly improve cross-scenario and cross-system generalization.