Abstract: A silhouette-based limb finder may be used to detect limbs from a camera image. This limb determination may be used to control an application, such as a game, or a combination with other image processing. A first distance field indicating a distance from the edge of a silhouette in an image and a second distance field indicating distance from a location in the silhouette may be used to generate a path from an extremity point on the silhouette to the location. This path then may be used to determine a limb in the silhouette. This allows tracking of limbs even for hard to detect player poses.

Abstract: A silhouette-based limb finder may be used to detect limbs from a camera image. This limb determination may be used to control an application, such as a game, or a combination with other image processing. A first distance field indicating a distance from the edge of a silhouette in an image and a second distance field indicating distance from a location in the silhouette may be used to generate a path from an extremity point on the silhouette to the location. This path then may be used to determine a limb in the silhouette. This allows tracking of limbs even for hard to detect player poses.

Abstract: Techniques for dynamic exercise content are described. In implementations, exercise content is provided that includes a variety of different selectable exercise segments that can be individually selected and played back to generate an exercise routine. For example, particular exercise segments can be selected based on user-specified exercise goals, the physical abilities of a particular user, based on various types of feedback, and so on. To assist in the selection of particular exercise segments, exercise segments can be individually tagged with descriptive information, such as using metadata tags. Embodiments can also provide a variety of different types of performance-related feedback to a user during an exercise routine.

Abstract: A silhouette-based limb finder may be used to detect limbs from a camera image. This limb determination may be used to control an application, such as a game, or a combination with other image processing. A first distance field indicating a distance from the edge of a silhouette in an image and a second distance field indicating distance from a location in the silhouette may be used to generate a path from an extremity point on the silhouette to the location. This path then may be used to determine a limb in the silhouette. This allows tracking of limbs even for hard to detect player poses.

Abstract: Techniques described herein use signal analysis to detect and analyze repetitive user motion that is captured in a 3D image. The repetitive motion could be the user exercising. One embodiment includes analyzing image data that tracks a user performing a repetitive motion to determine data points for a parameter that is associated with the repetitive motion. The different data points are for different points in time. A parameter signal of the parameter versus time that tracks the repetitive motion is formed. The parameter signal is divided into brackets that delineate one repetition of the repetitive motion from other repetitions of the repetitive motion. A repetition in the parameter signal is analyzed using a signal processing technique. Curve fitting and/or autocorrelation may be used to analyze the repetition.

Abstract: Techniques described herein determine a center of mass state vector based on a body model. The body model may be formed by analyzing a depth image of a user who is performing some motion. The center of mass state vector may include, for example, center-of-mass position, center-of-mass velocity, center-of-mass acceleration, orientation, angular velocity, angular acceleration, inertia tensor, and angular momentum. A center of mass state vector may be determined for an individual body part or for the body as a whole. The center of mass state vector(s) may be used to analyze the user's motion.

Abstract: Techniques described herein determine a center of mass state vector based on a body model. The body model may be formed by analyzing a depth image of a user who is performing some motion. The center of mass state vector may include, for example, center-of-mass position, center-of-mass velocity, center-of-mass acceleration, orientation, angular velocity, angular acceleration, inertia tensor, and angular momentum. A center of mass state vector may be determined for an individual body part or for the body as a whole. The center of mass state vector(s) may be used to analyze the user's motion.

Abstract: Techniques described herein use signal analysis to detect and analyze repetitive user motion that is captured in a 3D image. The repetitive motion could be the user exercising. One embodiment includes analyzing image data that tracks a user performing a repetitive motion to determine data points for a parameter that is associated with the repetitive motion. The different data points are for different points in time. A parameter signal of the parameter versus time that tracks the repetitive motion is formed. The parameter signal is divided into brackets that delineate one repetition of the repetitive motion from other repetitions of the repetitive motion. A repetition in the parameter signal is analyzed using a signal processing technique. Curve fitting and/or autocorrelation may be used to analyze the repetition.

Abstract: Disclosed herein are systems and methods for a runtime engine for analyzing user motion in a 3D image. The runtime engine is able to use different techniques to analyze the user's motion, depending on what the motion is. The runtime engine might choose a technique that depends on skeletal tracking data and/or one that instead uses image segmentation data to determine whether the user is performing the correct motion. The runtime engine might determine how to perform positional analysis or time/motion analysis of the user's performance based on what motion is being performed.

Abstract: Techniques for dynamic exercise content are described. In implementations, exercise content is provided that includes a variety of different selectable exercise segments that can be individually selected and played back to generate an exercise routine. For example, particular exercise segments can be selected based on user-specified exercise goals, the physical abilities of a particular user, based on various types of feedback, and so on. To assist in the selection of particular exercise segments, exercise segments can be individually tagged with descriptive information, such as using metadata tags. Embodiments can also provide a variety of different types of performance-related feedback to a user during an exercise routine.