Abstract:
Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.
Abstract:
“Real-world” gestures such as hand or finger movements/orientations that are generally recognized to mean certain things (e.g., an “OK” hand signal generally indicates an affirmative response) can be interpreted by a touch or hover sensitive device to more efficiently and accurately effect intended operations. These gestures can include, but are not limited to, “OK gestures,” “grasp everything gestures,” “stamp of approval gestures,” “circle select gestures,” “X to delete gestures,” “knock to inquire gestures,” “hitchhiker directional gestures,” and “shape gestures.” In addition, gestures can be used to provide identification and allow or deny access to applications, files, and the like.
Abstract:
The detection of an orientation of a stylus relative to a touch sensitive surface is disclosed. In one example, a touch image of the stylus tip and the hand used to grasp the stylus can be captured by the touch sensor panel and analyzed to determine the stylus' orientation relative to the surface of the touch sensor panel. The analysis can include estimating the size of the user's hand, determining the distance away from the user's hand at which the stylus tip makes contact with the touch sensor panel, and determining an angle of tilt based on the estimated size of the user's hand and the distance between the tip and the user's hand.
Abstract:
Embodiments are related to user input devices that accept complex user input including a combination of touch and push (or pick) input. Embodiments of the invention provide for selective ignoring or rejection of input received from such devices in order to avoid interpreting unintentional user actions as commands. Furthermore, some input signals can be modified. The selective rejection or modification can be performed by the user interface device itself or by a computing device that includes or is attached to the user interface device. The selective rejection or modification may be performed by a module that processes input signals, performs the necessary rejections and modifications and sends revised input signals to higher level modules.
Abstract:
Apparatus and methods are disclosed for simultaneously tracking multiple finger and palm contacts as hands approach, touch, and slide across a proximity-sensing, multi-touch surface. Identification and classification of intuitive hand configurations and motions enables unprecedented integration of typing, resting, pointing, scrolling, 3D manipulation, and handwriting into a versatile, ergonomic computer input device.
Abstract:
Apparatus and methods are disclosed for simultaneously tracking multiple finger and palm contacts as hands approach, touch, and slide across a proximity-sensing, multi-touch surface. Identification and classification of intuitive hand configurations and motions enables unprecedented integration of typing, resting, pointing, scrolling, 3D manipulation, and handwriting into a versatile, ergonomic computer input device.
Abstract:
The detection of finger pinch, rotate, and tap gestures along with a translation and optionally liftoff motion to initiate certain actions is disclosed. To detect both the gesture and the translation, a certain amount of gesture scaling speed can be detected along with a certain amount of translation speed and distance traveled. For a finger pinch gesture, the scaling speed can be computed as the dot product of the velocity vectors of two or more fingers coming together. For a finger rotation gesture, the scaling speed can be computed as a cross product of the velocity vectors of the rotating fingers. The translation speed of a gesture can be computed as the average of the velocity vectors of any fingers involved in the gesture. The amount of gesture scaling speed and translation speed needed to trigger the recognition of a combined gesture with translation can be a predetermined ratio.
Abstract:
A touch sensitive device that detects the occurrence of an electrostatic discharge event on the device by analyzing an acquired touch image for characteristics associated with the occurrence of an ESD event is provided. An acquired touch image is analyzed for characteristics that differentiate it from a touch image generated by a user input and are correlated with an expected touch image generated by an ESD event.
Abstract:
Techniques for identifying and discriminating between different types of contacts to a multi-touch touch-screen device are described. Illustrative contact types include fingertips, thumbs, palms and cheeks. By way of example, thumb contacts may be distinguished from fingertip contacts using a patch eccentricity parameter. In addition, by non-linearly deemphasizing pixels in a touch-surface image, a reliable means of distinguishing between large objects (e.g., palms) from smaller objects (e.g., fingertips, thumbs and a stylus) is described.