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.