  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.
