Abstract: Systems and methods are disclosed for providing a virtual mouse for a computing device have a touchscreen. A first placement region of the touchscreen may be determined. The first placement region may then be determined to contain a first portion of at least one touch target. The first portion of the at least one touch target may then be deactivated. A virtual mouse may then be activated at the first placement region.

Abstract: In various examples, the present disclosure describes methods and systems for generating hand state predictions. A hand state prediction system includes a machine learning-based model, such as a neural network model, that is trained to convert inertial motion measurements and surface contact data into predictions of a corresponding hand gesture or gripping posture. For each window of data sampled, motion data and contact data are obtained, processed and fused to generate a fused prediction. The hand state prediction system can operate in continuous mode to automatically detect a start and an end of a hand action, or a user can designate a start and an end of a hand action. A hand state prediction is generated by a multimodal classifier by processing the fused prediction. Instructions represented by a hand action can be acted upon through a command action performed by a computing device or a computer application.

Abstract: A method, a feedback-enabled device and a computer-readable memory are provided, for generating haptic feedback. The actuators of a tactile surface are vibrated for producing vibration bursts based on a sequence of an adaptive friction grain signal. The adaptive friction grain signal comprises a plurality of grains corresponding to the vibration bursts, each grain being defined by an amplitude. The adaptive friction grain signal has a grain density that is variable over the sequence.