Abstract: Techniques are disclosed pertaining to determining a level of proficiency of a user at a computer simulation through the use of a plurality of skill assessment tests. Each skill assessment test can test a respective skill for use in the computer simulation. The techniques can also be used to train a user for the computer simulation and/or to determine team composition using roles for use in the computer simulation. In some aspects, skill assessment tests may incorporate multiple iterations in including a first base line iteration and subsequent iterations that differ between user devices. In some embodiments, metrics that are indicative of a level of proficiency of the user can be made with respect to metrics of other users to determine a user's level of skill with respect to various groups of other users.

Abstract: A system for evaluating a posture of a user operating a computing device can include one or more processors, a sensor suite configured to generate sensor data corresponding to a three-dimensional (3D) orientation of a user's body, and one or more machine-readable, non-transitory storage mediums that include instructions configured to cause the one or more processors to perform operations including: estimating the user's posture based on the sensor data from the sensor suite; receiving application data corresponding to an application that the user is interfacing with on the computing device; and generating a classification of the user's posture based on a comparison of the estimated posture with a plurality of posture types. The performed operations may further include determining a recommendation to modify and improve the user's posture based on the classification and the application data and generating a user-accessible output that corresponds to the recommendation.

Abstract: Techniques are disclosed pertaining to determining a level of proficiency of a user at a computer simulation through the use of a plurality of skill assessment tests. Each skill assessment test can test a respective skill for use in the computer simulation. The techniques can also be used to train a user for the computer simulation and/or to determine team composition using roles for use in the computer simulation. In some aspects, skill assessment tests may incorporate multiple iterations in including a first base line iteration and subsequent iterations that differ between user devices. In some embodiments, metrics that are indicative of a level of proficiency of the user can be made with respect to metrics of other users to determine a user's level of skill with respect to various groups of other users.

Abstract: Techniques are disclosed pertaining to determining a level of proficiency of a user at a computer simulation through the use of a plurality of skill assessment tests. Each skill assessment test can test a respective skill for use in the computer simulation. The techniques can also be used to train a user for the computer simulation and/or to determine team composition using roles for use in the computer simulation. In some aspects, skill assessment tests may incorporate multiple iterations in including a first base line iteration and subsequent iterations that differ between user devices. In some embodiments, metrics that are indicative of a level of proficiency of the user can be made with respect to metrics of other users to determine a user's level of skill with respect to various groups of other users.

Abstract: Techniques are disclosed pertaining to determining a level of proficiency of a user at a computer simulation through the use of a plurality of skill assessment tests. Each skill assessment test can test a respective skill for use in the computer simulation. The techniques can also be used to train a user for the computer simulation and/or to determine team composition using roles for use in the computer simulation. In some aspects, skill assessment tests may incorporate multiple iterations in including a first base line iteration and subsequent iterations that differ between user devices. In some embodiments, metrics that are indicative of a level of proficiency of the user can be made with respect to metrics of other users to determine a user's level of skill with respect to various groups of other users.

Abstract: An AR/VR input device include a processor(s), an internal measurement unit (IMU), and a plurality of sensors configured to detect emissions received from a plurality of remote emitters. The processor(s) can be configured to: determine a time-of-flight (TOF) of the detected emissions, determine a first estimate of a position and orientation of the input device based on the TOF of a subset of the detected emissions and the particular locations of each of the plurality of sensors on the input device that are detecting the detected emissions, determine a second estimate of the position and orientation of the input device based on the measured acceleration and velocity from the IMU, and continuously update a calculated position and orientation of the input device within the AR/VR environment in real-time based on a Beyesian estimation (e.g., Extended Kalman filter) that utilizes the first estimate and second estimate.

Abstract: An AR/VR input device include a processor(s), an internal measurement unit (IMU), and a plurality of sensors configured to detect emissions received from a plurality of remote emitters. The processor(s) can be configured to: determine a time-of-flight (TOF) of the detected emissions, determine a first estimate of a position and orientation of the input device based on the TOF of a subset of the detected emissions and the particular locations of each of the plurality of sensors on the input device that are detecting the detected emissions, determine a second estimate of the position and orientation of the input device based on the measured acceleration and velocity from the IMU, and continuously update a calculated position and orientation of the input device within the AR/VR environment in real-time based on a Beyesian estimation (e.g., Extended Kalman filter) that utilizes the first estimate and second estimate.

Abstract: An AR/VR input device include a processor(s), an internal measurement unit (IMU), and a plurality of sensors configured to detect emissions received from a plurality of remote emitters. The processor(s) can be configured to: determine a time-of-flight (TOF) of the detected emissions, determine a first estimate of a position and orientation of the input device based on the TOF of a subset of the detected emissions and the particular locations of each of the plurality of sensors on the input device that are detecting the detected emissions, determine a second estimate of the position and orientation of the input device based on the measured acceleration and velocity from the IMU, and continuously update a calculated position and orientation of the input device within the AR/VR environment in real-time based on a Beyesian estimation (e.g., Extended Kalman filter) that utilizes the first estimate and second estimate.