Kamran Ali, et al.:
In this paper, we propose a WiFi signal based keystroke recognition system called WiKey. WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%.
From the paper (PDF):
In this paper, we have shown that fine grained activity recognition is possible by using COTS WiFi devices. Thus, the techniques proposed in this paper can be used for several HCI applications. Examples include zoom-in, zoom-out, scrolling, sliding, and rotating gestures for operating personal computers, gesture recognition for gaming consoles, in-home gesture recognition for operating various household devices, and applications such as writing and drawing in the air.
The paper does say that the initial research was done in a very controlled environment; the amount of noise created by someone walking between the WiFi sender and receiver, for example, could cause a drop in accuracy and reliability. Utterly fascinating, nevertheless.