Abstract:
Exemplary embodiments may involve analyzing reflections from an eye to help determine where the respective sources of the reflections are located. An exemplary method may involve: (a) analyzing eye-image data to determine observed movement of a reflected feature on a corneal surface; (b) determining an expected movement of the reflected feature on the corneal surface given a value of a z-distance parameter, wherein the value of the z-distance parameter is initially set at a first value; (c) determining a difference between the observed movement of the reflected feature on the corneal surface and the expected movement of the reflected feature on the corneal surface; (d) if the difference is less than a threshold, then associating the value of the z-distance parameter with a source of the reflected feature; and (e) if the difference is greater than the threshold, then: (i) making a predetermined adjustment to the value of the z-distance parameter; and (ii) repeating (a) to (d) with the adjusted value of the z-distance parameter.
Abstract:
Methods, apparatus, and computer-readable media are described herein related to recognizing a look up gesture. Level-indication data from at least an accelerometer associated with a wearable computing device (WCD) can be received. The WCD can be worn by a wearer. The WCD can determine whether a head of the wearer is level based on the level-indication data. In response to determining that the head of the wearer is level, the WCD can receive lookup-indication data from at least the accelerometer. The WCD can determine whether the head of the wearer is tilted up based on the lookup-indication data. In response to determining that the head of the wearer is tilted up, the WCD can generate a gesture-recognition trigger, where the gesture-recognition trigger indicates that the head of the wearer has moved up from level.
Abstract:
Example methods and systems for determining correlated movements associated with movements caused by driving a vehicle are provided. In an example, a computer-implemented method includes identifying a threshold number of sets of correlated movements. The method further includes determining that the threshold number of sets of correlated movements is associated with movements caused by driving a vehicle. The method still further includes causing the wearable computing system to select a driving user interface for the wearable computing system.
Abstract:
In one example, a method includes outputting, by a computing device and for display, a graphical user interface comprising a first graphical keyboard comprising a first plurality of keys. The method further includes determining, based at least in part on an input context, to output a second graphical keyboard comprising a second plurality of keys, and outputting, for contemporaneous display with the first graphical keyboard, the second graphical keyboard. A character associated with at least one key from the second plurality of keys may be different than each character associated with each key from the first plurality of keys. The method further includes selecting, based at least in part on a first portion of a continuous gesture, a first key from first graphical keyboard, and selecting, based at least in part on a second portion of the continuous gesture, a second key from the second graphical keyboard.