Writing in the air: Recognize Letters Using Deep Learning Through WiFi Signals

Device-free handwriting recognition, which can be widely applied to Human–Computer Interaction (HCI), is expected to monitor the handwriting change of entities without actively involving any physical devices. The key intuition is that different handwriting movements induce different multi-path distortions in WiFi signals and generate distinct patterns in the time-series of Channel State Information (CSI). In this paper, a proposed WiFi-based system namely Wima, by means of which, we are in pursuit of an advanced solution to recognize letters written by hand in the air. Unlike existing WiFi-based handwriting recognition systems, Wima extracts features directly from the original CSI and then transmit them to subsequent classifiers immediately, a deep-learning-based method is firstly introduced in our system to automatically extract high-level features. More specifically, Wima transforms CSI characters like amplitude and phase into images, which are afterwards delivered to extract features by adopting Convolutional Neural Networks (CNN) respectively. The final simulation with commercial IEEE 802.11n NICs shows that Wima can achieve air-writing recognition with a better performance when compared with some existing routine approaches.