Abstract: Methods, devices, and media are disclosed for mapping of input and output spaces in head-based human-computer interactions. In some embodiments, an end-to-end method is described for designing a head-based user interface, calibrating the interface to individual users, and interacting with a user in real time by mapping head-based user inputs to an output space in a way that optimizes the target selection efficiency of the interaction. Head orientation may be leveraged to define the mapping between the user input and the output space.

Abstract: Methods, systems and media for context-aware estimation of student attention in online learning are described. An attention monitoring system filters or restricts the time periods in which student attention is monitored or assessed to those time periods in which student attention is important. These time periods of high attention importance may be determined by processing data from the teacher, such as audio data representing the teacher's voice and/or visual presentation data representing slides or other visual material being presented to the students. Various types of presenter data from the teacher and attendee data from the students may be used in assessing the importance of attention and each student's attention during each time period. The presenter may be provided with feedback in various forms showing student attention performance aggregated or segmented according to various criteria.

Abstract: A gesture-controlled device and a method thereon are provided. The method includes capturing a plurality of video frames of a user's body and processing the plurality of video frames to allow detecting a portion of the user's body and to allow recognizing hand gestures. In response to detecting the portion of the user's body, the method includes generating at least one widget interaction region corresponding to the portion of the user's body. The method further includes recognizing, a mid-air hand gesture in the at least one widget interaction region, mapping the mid-air hand gesture to at least one virtual widget on the gesture-controlled device, and manipulating the at least one virtual widget based on the mid-air hand gesture. The method and device allow manipulation of virtual widgets using mid-air gestures on a gesture-controlled device.

Abstract: Methods, devices, and media are disclosed for mapping of input and output spaces in head-based human-computer interactions. In some embodiments, an end-to-end method is described for designing a head-based user interface, calibrating the interface to individual users, and interacting with a user in real time by mapping head-based user inputs to an output space in a way that optimizes the target selection efficiency of the interaction. Head orientation may be leveraged to define the mapping between the user input and the output space.

Abstract: A gesture-controlled device and a method thereon are provided. The method includes capturing a plurality of video frames of a user's body and processing the plurality of video frames to allow detecting a portion of the user's body and to allow recognizing hand gestures. In response to detecting the portion of the user's body, the method includes generating at least one widget interaction region corresponding to the portion of the user's body. The method further includes recognizing, a mid-air hand gesture in the at least one widget interaction region, mapping the mid-air hand gesture to at least one virtual widget on the gesture-controlled device, and manipulating the at least one virtual widget based on the mid-air hand gesture. The method and device allow manipulation of virtual widgets using mid-air gestures on a gesture-controlled device.

Abstract: A system including neural network that use deep learning for object detection allows users to interact with images of their face and, in particular, interact with different face landmarks as an input channel for interaction with smart devices. That is, the face is employed as a touch surface for interactions with a device.

Abstract: Methods and apparatus for gesture-based control of a device in a multi-user environment are described. The methods prioritize users or gestures based on a predetermined priority ruleset. A first-user-in-time ruleset prioritizes gestures based on when in time they were begun by a user in the camera FOV. An action-hierarchy ruleset prioritizes gestures based on the actions they correspond to, and the relative positions of those actions within an action hierarchy. A designated-master-user ruleset prioritizes gestures performed by an explicitly designated master user. Methods for designating a new master user and for providing gesture-control-related user feedback in a multi-user environment are also described.

Abstract: Methods and apparatus for gesture-based control of a device in a multi-user environment are described. The methods prioritize users or gestures based on a predetermined priority ruleset. A first-user-in-time ruleset prioritizes gestures based on when in time they were begun by a user in the camera FOV. An action-hierarchy ruleset prioritizes gestures based on the actions they correspond to, and the relative positions of those actions within an action hierarchy. A designated-master-user ruleset prioritizes gestures performed by an explicitly designated master user. Methods for designating a new master user and for providing gesture-control-related user feedback in a multi-user environment are also described.

Abstract: Methods and apparatus for gesture-based control of a device in a multi-user environment are described. The methods prioritize users or gestures based on a predetermined priority ruleset. A first-user-in-time ruleset prioritizes gestures based on when in time they were begun by a user in the camera FOV. An action-hierarchy ruleset prioritizes gestures based on the actions they correspond to, and the relative positions of those actions within an action hierarchy. A designated-master-user ruleset prioritizes gestures performed by an explicitly designated master user. Methods for designating a new master user and for providing gesture-control-related user feedback in a multi-user environment are also described.

Abstract: Methods and apparatus for gesture-based control of a device in a multi-user environment are described. The methods prioritize users or gestures based on a predetermined priority ruleset. A first-user-in-time ruleset prioritizes gestures based on when in time they were begun by a user in the camera FOV. An action-hierarchy ruleset prioritizes gestures based on the actions they correspond to, and the relative positions of those actions within an action hierarchy. A designated-master-user ruleset prioritizes gestures performed by an explicitly designated master user. Methods for designating a new master user and for providing gesture-control-related user feedback in a multi-user environment are also described.

Abstract: A system including neural network that use deep learning for object detection allows users to interact with images of their face and, in particular, interact with different face landmarks as an input channel for interaction with smart devices. That is, the face is employed as a touch surface for interactions with a device.

Abstract: A system including neural network that use deep learning for object detection allows users to interact with images of their face and, in particular, interact with different face landmarks as an input channel for interaction with smart devices. That is, the face is employed as a touch surface for interactions with a device.

Abstract: A system including neural network that use deep learning for object detection allows users to interact with images of their face and, in particular, interact with different face landmarks as an input channel for interaction with smart devices. That is, the face is employed as a touch surface for interactions with a device.