Using GAN to Enhance the Accuracy of Indoor Human Activity Recognition

TL;DR

This paper introduces a semi-supervised learning approach using GAN-generated synthetic CSI data to improve indoor human activity recognition accuracy, reducing data collection costs and processing time.

Contribution

It presents a novel combination of GANs with LSTM-based HAR systems to enhance classification performance with less real data.

Findings

Classification accuracy increased by 3.4%.

Log loss reduced by nearly 16%.

Synthetic data effectively supplements real CSI data.

Abstract

Indoor human activity recognition (HAR) explores the correlation between human body movements and the reflected WiFi signals to classify different activities. By analyzing WiFi signal patterns, especially the dynamics of channel state information (CSI), different activities can be distinguished. Gathering CSI data is expensive both from the timing and equipment perspective. In this paper, we use synthetic data to reduce the need for real measured CSI. We present a semi-supervised learning method for CSI-based activity recognition systems in which long short-term memory (LSTM) is employed to learn features and recognize seven different actions. We apply principal component analysis (PCA) on CSI amplitude data, while short-time Fourier transform (STFT) extracts the features in the frequency domain. At first, we train the LSTM network with entirely raw CSI data, which takes much more processing time. To this end, we aim to generate data by using 50% of raw data in conjunction with a generative adversarial network (GAN). Our experimental results confirm that this model can increase classification accuracy by 3.4% and reduce the Log loss by almost 16% in the considered scenario.