# Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN

arXiv: https://arxiv.org/abs/2212.13161 · HTML: https://mlsanity.com/paper/2212.13161/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 26 Dec 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1805.08620/) (2018)
- [WiGest: A Ubiquitous WiFi-based Gesture Recognition System](https://mlsanity.com/paper/1501.04301/) (2015)
- [CoMuTe: A Convolutional Neural Network Based Device Free Multiple Target Localization Using CSI](https://mlsanity.com/paper/2003.05734/) (2020)

## Built on by (most cited first, 2 of 2)

- [Quantum Machine Learning Applied to the Classification of Diabetes](https://mlsanity.com/paper/2301.00109/) (2022)
- [Physics-Guided Attention in a Lightweight TCN for Efficient WiFi CSI-Based Human Activity Recognition](https://mlsanity.com/paper/2606.01834/) (2026)

## Abstract (arXiv)

Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) -- a novel approach that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms -- the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used the Wavelet CNN for classification, which is a deep convolutional network analogous to the well-studied ResNet and DenseNet networks. We empirically show that our proposed PCWCNN model performs very well on a real dataset, outperforming existing approaches.