This paper explores recovering victim key-presses through a Wi-Fi data channel know as Beam-forming Feedback Information. BFI is used to help wireless APs adjust their beam-forming TX to improve performance, but BFI contains data correlated by changes in device orientation, and the attenuation from nearby movement (e.g., fingers on keyboard). By training a NN, the researchers were able to recover numeric key-presses (from a numeric keyboard) with ~88% accuracy across a variety of devices.

Pretty impressive, and shows how difficult it is to account for side-channels across all the layers of the stack when it's relatively easy to train a very sensitive ML model to extract a tiny signal from the noise.