In recent years, the use of WiFi Channel State Information (CSI) for Human Activity Recognition (HAR) has attracted widespread attention, thanks to its low cost and non-intrusive advantages. Previous research mostly used models based on Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) for activity recognition. However, these methods fail to achieve good parallelism while learning global features and fine-grained features, so they often cannot achieve the ideal recognition effect or training speed. In light of this, we propose an ensemble deep learning model based on CNN a...
