Abstract: The disclosure relates to a system for evaluating movement of a body of a user. The system may include a video display, one or more digital cameras, and a processor. The processor may control the one or more cameras to generate images of at least the part of the body over a period of time. The processor may estimate a position of a plurality of joints of the body. The processor may receive a selection of a tracked pose, and determine, from the plurality of joints, a set of joints associated with the tracked pose. The processor may generate at least one joint vector connecting joints in the set of joints, and assign, based on changes in the joint vector over the period of time, a form score to a performance of the tracked pose. The processor may then generate a user interface that depicts the form score.

Abstract: The disclosure relates to a system for evaluating movement of a body of a user. The system may include a video display, one or more digital cameras, and a processor. The processor may control the one or more cameras to generate images of at least the part of the body over a period of time. The processor may estimate a position of a plurality of joints of the body. The processor may receive a selection of a tracked pose, and determine, from the plurality of joints, a set of joints associated with the tracked pose. The processor may generate at least one joint vector connecting joints in the set of joints, and assign, based on changes in the joint vector over the period of time, a form score to a performance of the tracked pose. The processor may then generate a user interface that depicts the form score.

Abstract: The disclosure relates to a system for evaluating movement of a body of a user. The system may include a video display, one or more digital cameras, and a processor. The processor may control the one or more cameras to generate images of at least the part of the body over a period of time. The processor may estimate a position of a plurality of joints of the body. The processor may receive a selection of a tracked pose, and determine, from the plurality of joints, a set of joints associated with the tracked pose. The processor may generate at least one joint vector connecting joints in the set of joints, and assign, based on changes in the joint vector over the period of time, a form score to a performance of the tracked pose. The processor may then generate a user interface that depicts the form score.

Abstract: The disclosure relates to a system for providing guidance for positioning a body. The system may include a video display, one or more digital cameras configured to generate a depth video stream and a visual video stream, and a computing device including, a memory, and a processor. The processor may control the one or more digital cameras to generate the depth video stream including a depth image of the body and the visual video stream including a color image of the body. The processor identifies at least a part of the body within the images using a first trained learning machine to segment the images and isolate the body. The processor may crop both the visual image and the depth image based on the identified body. The processor may estimate a position of a plurality of joints of the body by applying a second trained learning machine to the identified and isolated part of the body. The processor may generate a current pose estimate by connecting estimated positions of the plurality of joints.

Abstract: Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples.

Abstract: Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples.

Abstract: Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples.

Abstract: Methods and systems for creating ensembles of hypersurfaces in high-dimensional feature spaces, and to machines and systems relating thereto. More specifically, exemplary aspects of the invention relate to methods and systems for generating supervised hypersurfaces based on user domain expertise, machine learning techniques, or other supervised learning techniques. These supervised hypersurfaces may optionally be combined with unsupervised hypersurfaces derived from unsupervised learning techniques. Lower-dimensional subspaces may be determined by the methods and systems for creating ensembles of hypersurfaces in high-dimensional feature spaces. Data may then be projected onto the lower-dimensional subspaces for use, e.g., in further data discovery, visualization for display, or database access.

Abstract: Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples.

Abstract: Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples.

Abstract: Methods and systems for creating ensembles of hypersurfaces in high-dimensional feature spaces, and to machines and systems relating thereto. More specifically, exemplary aspects of the invention relate to methods and systems for generating supervised hypersurfaces based on user domain expertise, machine learning techniques, or other supervised learning techniques. These supervised hypersurfaces may optionally be combined with unsupervised hypersurfaces derived from unsupervised learning techniques. Lower-dimensional subspaces may be determined by the methods and systems for creating ensembles of hypersurfaces in high-dimensional feature spaces. Data may then be projected onto the lower-dimensional subspaces for use, e.g., in further data discovery, visualization for display, or database access.

Abstract: Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples.