Human Activity Recognition Based on the GRU with Augmented Wi-Fi CSI Signals

Hari Kang, Donghyun Kim, Kar-Ann Toh

Abstract: This study introduces an innovative Human Activity Recognition system using Wi-Fi Channel State Information (CSI). Our modified GRU input enhances representation capability by integrating the past input information. The system employs three data augmentation techniques— Adding Gaussian Noise, Data Shifting, and CutMix— to expand the dataset and introduce variability for overfitting handling. We conducted three key experiments to validate our model's performance: optimizing hyperparameters, performing ablation study to assess each technique's impact, and comparing our model with state-of-the-art models. 11The implemented codes are available in https://github.com/GRUwithAugmentedData

External IDs:dblp:conf/tencon/Kang0T24