A Survey of Human Activity Recognition Using WiFi CSI

TL;DR

This survey reviews recent methods for passive human activity recognition using WiFi CSI, highlighting the shift from traditional machine learning to deep learning approaches like LSTM RNN for improved accuracy.

Contribution

The paper provides a comprehensive overview of existing techniques and introduces the potential of deep learning models, particularly LSTM RNNs, for enhanced human activity recognition using WiFi CSI.

Findings

Deep learning models outperform traditional machine learning in CSI-based activity recognition.

LSTM RNNs effectively capture temporal dependencies in CSI data.

Challenges include environment variability and multi-user scenarios.

Abstract

In this article, we present a survey of recent advances in passive human behaviour recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. Movement of human body causes a change in the wireless signal reflections, which results in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behaviour can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performances, however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural network (RNN), and show the improved performance. We also discuss about different challenges such as environment change, frame rate selection, and multi-user scenario, and suggest possible directions for future work.