LiKey: Location-independent keystroke recognition on numeric keypads using WiFi signal

As an application of wireless sensing, keystroke recognition can be used in a variety of scenarios, such as password protection and analog input for devices. Existing WiFi-based keystroke recognition systems have strong dependencies on environment and location. Once the environment or keyboard position changes, systems need to re-collect data and re-train the model, otherwise the performance will degrade significantly.In this paper, we propose LiKey, a location-independent keystroke recognition system for numeric keypads, consisting of one transmitter and two receivers. We specially place the transceiver devices, which makes it easy to analyze the variation of the reflection path length for keystrokes at different locations. The same keystroke at different positions has the same change rule of reflection path length, corresponding to the same change rule of rotational direction in the channel state information (CSI) complex plane. We design a method named SRDF to sum the rotational direction features in the CSI complex plane and obtain the location independent features. Since different keystrokes behave differently on the two receivers, a dual-receiver phase–amplitude joint optimization segmentation algorithm is designed to extract the complete keystroke segments. Finally, the dual-branch CNN model is used for training and classification based on the features of the two receivers. The experimental results show that LiKey has strong robustness to the keystroke position. It recognizes well even for keystrokes on positions not involved in the training.