Abstract: A mechanism for interpreting spatiotemporal data from augmented and virtual reality devices into fitness related metrics and recommendations. Spatiotemporal data can be compared to the physical data of the end user to interpret energy expense such as estimated calorie burn. A virtual reality or augmented reality device can report positional data over time as the user engages with a corresponding environment. The positional data is compared to a biometric profile of the end user to understand how their physical body is exerting itself. The spatiotemporal data is a series of coordinate data expressed over time. The biometric data is the physical profile of the end user. This data is used to understand and quantify physical movement while using a virtual reality or augmented reality device. The energy expense determination mechanism can be used to provide a virtual coach for activities and workouts, games involving physical movement, and other applications.

Abstract: A mechanism for interpreting spatiotemporal data from augmented reality devices and virtual reality devices based on an analysis performed by one or more predictive machines. The predictive machines may implement machine learning techniques and/or algorithms to predict a heart rate for a user, from which user calorie expense can be calculated. In operation, a virtual reality or augmented reality device can receive positional data over time, for example derived from image data from a camera. The positional data can be averaged over time, and the averages can be rolled-up, and the averaged and rolled-up data can be fed into a predictive machine to generate a heart rate prediction. The heart rate prediction is used to generate a user heart rate, from which calories for the user's corresponding motion is determined.

Abstract: A mechanism for interpreting spatiotemporal data from augmented and virtual reality devices into fitness related metrics and recommendations. Spatiotemporal data can be compared to the physical data of the end user to interpret energy expense such as estimated calorie burn. A virtual reality or augmented reality device can report positional data over time as the user engages with a corresponding environment. The positional data is compared to a biometric profile of the end user to understand how their physical body is exerting itself. The spatiotemporal data is a series of coordinate data expressed over time. The biometric data is the physical profile of the end user. This data is used to understand and quantify physical movement while using a virtual reality or augmented reality device. The energy expense determination mechanism can be used to provide a virtual coach for activities and workouts, games involving physical movement, and other applications.

Abstract: A mechanism for interpreting spatiotemporal data from augmented reality devices and virtual reality devices based on an analysis performed by one or more predictive machines. The predictive machines may implement machine learning techniques and/or algorithms to predict a heart rate for a user, from which user calorie expense can be calculated. In operation, a virtual reality or augmented reality device can receive positional data over time, for example derived from image data from a camera. The positional data can be averaged over time, and the averages can be rolled-up, and the averaged and rolled-up data can be fed into a predictive machine to generate a heart rate prediction. The heart rate prediction is used to generate a user heart rate, from which calories for the user's corresponding motion is determined.

Abstract: A mechanism for interpreting spatiotemporal data from augmented and virtual reality devices into fitness related metrics and recommendations. Spatiotemporal data can be compared to the physical data of the end user to interpret energy expense such as estimated calorie burn. A virtual reality or augmented reality device can report positional data over time as the user engages with a corresponding environment. The positional data is compared to a biometric profile of the end user to understand how their physical body is exerting itself. The spatiotemporal data is a series of coordinate data expressed over time. The biometric data is the physical profile of the end user. This data is used to understand and quantify physical movement while using a virtual reality or augmented reality device. The energy expense determination mechanism can be used to provide a virtual coach for activities and workouts, games involving physical movement, and other applications.

Abstract: A deployable mixed reality environment system may include one or more panels. The panels may be positioned or coupled together to form an interior chamber into which a user may be positioned. An actual reality recorder may record a user within the interior chamber and their movements. A system computer may be in communication with a virtual reality headset, with a user input controller, and with the actual reality recorder. The system computer may create and display a virtual reality environment to a user through the virtual reality headset while allowing the user to interact with the virtual reality environment through the user input controller. The system computer may also record the user within the interior chamber and then overlay the virtual reality environment displayed to the user onto the panels, thereby forming a mixed reality which includes the user superimposed into the virtual reality environment.