Wi-Fi Based Gesture Recognition Using Deep Transfer Learning

This paper presents a deep transfer learning based gesture recognition approach using Channel State Information (CSI). In our method, the amplitude of each CSI stream is reorganized as an image matrix. Then Wi-Fi based gesture recognition is cast into image classification framework. The shift to an image classification paradigm provides the advantage of using an existing Convolutional Neural Network (CNN) model pre-trained on a very large number of images. Furthermore, transfer learning model is employed to eliminate the need for a lengthy network training process and to extract more suitable features for the CSI matrix. Finally, we test our method on a dataset collected by ourselves, and the result shows that our method has a better performance than state-of-the-art in Wi-Fi based gesture recognition.