Abstract: System and techniques for positional analysis using computer vision sensor synchronization are described herein. A set of sensor data may be obtained for a participant of an activity. A video stream may be captured in response to detection of a start of the activity in the set of sensor data. The video stream may include images of the participant engaging in the activity. A key stage of the activity may be identified by evaluation of the sensor data. A key frame may be selected from the video stream using a timestamp of the sensor data used to identify the key stage of the activity. A skeletal map may be generated for the participant in the key frame using key points of the participant extracted from the key frame. Instructional data may be selected using the skeletal map. The instructional data may be displayed on a display device.

Abstract: System and techniques for positional analysis using computer vision sensor synchronization are described herein. A set of sensor data may be obtained for a participant of an activity. A video stream may be captured in response to detection of a start of the activity in the set of sensor data. The video stream may include images of the participant engaging in the activity. A key stage of the activity may be identified by evaluation of the sensor data. A key frame may be selected from the video stream using a timestamp of the sensor data used to identify the key stage of the activity. A skeletal map may be generated for the participant in the key frame using key points of the participant extracted from the key frame. Instructional data may be selected using the skeletal map. The instructional data may be displayed on a display device.

Abstract: System and techniques for positional analysis using computer vision sensor synchronization are described herein. A set of sensor data may be obtained for a participant of an activity. A video stream may be captured in response to detection of a start of the activity in the set of sensor data. The video stream may include images of the participant engaging in the activity. A key stage of the activity may be identified by evaluation of the sensor data. A key frame may be selected from the video stream using a timestamp of the sensor data used to identify the key stage of the activity. A skeletal map may be generated for the participant in the key frame using key points of the participant extracted from the key frame. Instructional data may be selected using the skeletal map. The instructional data may be displayed on a display device.

Abstract: Apparatuses, methods and storage medium associated with the provision of an avatar keyboard to a communication/computing device are disclosed herein. In embodiments, an apparatus for communicating may comprise one or more processors to execute an application; and a keyboard module coupled with the one or more processor to provide a plurality of keyboards in a corresponding plurality of keyboard modes for inputting to the application, including an avatar keyboard, in an avatar keyboard mode. The avatar keyboard may include a plurality of avatar keys with corresponding avatars that can be dynamically customized or animated based at least in part on facial expressions or head poses of a user, prior to input to the application. Other embodiments may be disclosed and/or claimed.

Abstract: Apparatuses, methods and storage medium associated with capturing images are disclosed herein. In embodiments, the apparatus may include a face tracker to receive an image frame, analyze the image frame for a face, and on identification of a face in the image frame, evaluate the face to determine whether the image frame comprises an acceptable or unacceptable face pose. Further, the face tracker may be configured to provide instructions for taking another image frame, on determination of the image frame having an unacceptable face pose, with the instructions designed to improve likelihood that the other image frame will comprise an acceptable face pose. Other embodiments may be described and/or claimed.