Using GAN to Enhance the Accuracy of Indoor Human Activity Recognition

Understand

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.