Bertec Corporation

Mohan Chandra Baro

Necip Berme

Emre Akbas

Batuhan Karagoz

Cameron Scott Hobson

Ozhan Suat

Murat Kerim Berme

Jaswandi Tushar Pitale

Fernando Vanderlinde Dos Santos

Bedirhan Uguz

Megan Elaine Cotterman

Sasan Ghassab

Aysun Dolan

Utku Aktas

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving the one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a three dimensional pose estimation neural network; (iii) generating, by using the three dimensional pose estimation neural network, vertices of a canonical human mesh model for the one or more images of the scene; (iv) retrieving a weight matrix from an annotation server that corresponds to a desired keypoint set; and (v) generating the desired keypoint set by multiplying the retrieved weight matrix with the vertices of the canonical human mesh model.

System for estimating a three dimensional pose of one or more persons in a scene

A swing analysis system is disclosed herein. The swing analysis system includes at least one user input device; a motion capture system comprising at least one motion capture device configured to detect the motion of at least one of: (i) one or more body segments of a subject, (ii) a head and/or face of the subject, (iii) a hand and/or fingers of the subject, and (iv) an object being manipulated by the subject, and generate output data; and at least one data processing device operatively coupled to the user input device and the motion capture system, the at least one data processing device being programmed to perform autodetection for the movement of the subject that is selected by the user by inputting the output data from the motion capture system into a trained neural network so that the movement being performed by the subject is able to be automatically determined.

Swing analysis system

A head-mounted display system is disclosed herein. In one or more embodiments, a system for body pose estimation in a virtual reality or augmented reality environment includes one or more egocentric cameras coupled to a user configured to capture video data from a perspective of the user; an instrumented flooring system including one or more sensors configured to detect foot placement and/or movement of the user, and generate sensor output data based on the detected foot placement and/or movement of the user; and at least one data processing device being configured to receive and integrate the video data from the one or more egocentric cameras and the sensor output data from the instrumented flooring system using sensor fusion algorithms, thereby generating a comprehensive body pose estimation for the user.

Head-mounted display system

A system for testing and/or training the vision of a user is disclosed herein. The system includes at least one camera, a visual display device having an output screen, and a data processing device operatively coupled to the at least one camera and the visual display device. In one embodiment, the data processing device is programmed to determine a head position, head velocity, and/or head speed of a user during a vision test or vision training routine from a plurality of images of the head of the user captured by the at least one camera. In another embodiment, the data processing device is programmed to determine, based upon an input signal received from a user input device, a contrast display setting for a screen background relative to at least one visual target, the contrast display setting enabling the user to gradually adapt to increasing levels of visual stimulation.

System for testing and/or training the vision of a user

A system for training neural networks that predict the parameters of a human mesh model is disclosed herein. The system includes at least one camera and a data processor configured to execute computer executable instructions for: receiving a first frame and a second frame of a video from the at least one camera; extracting first and second image data from the first and second frames of the video; inputting the sequence of frames of the video into a human mesh estimator module, the human mesh estimator module estimating mesh parameters from the sequence of frames of the video so as to determine a predicted mesh; and generating a training signal for input into the human mesh estimator module by using a two-dimensional keypoint loss module that compares a first set of two-dimensional image-based keypoints to a second set of two-dimensional model-based keypoints.

System for training neural networks that predict the parameters of a human mesh model using dense depth and part-based UV map annotations

A measurement and testing system is disclosed herein. The measurement and testing system includes one or more first devices at a first location operatively coupled to at least one first data processing device, and one or more second devices at a second location operatively coupled to at least one second data processing device. The at least one first data processing device being configured to execute stored instructions for: generating one or more motion profiles for controlling the one or more first devices at the first location; controlling the one or more first devices at the first location based upon the one or more motion profiles; recording the one or more motion profiles at the first location; and transmitting the one or more recorded motion profiles to the second location. The at least one second data processing device being configured to execute stored instructions for receiving the one or more recorded motion profiles from the at least one first data processing device at the first location; and controlling the one or more second devices at the second location based upon the one or more recorded motion profiles from the first location.

Measurement and testing system

A force measurement system is disclosed herein. The force measurement system includes at least one user input device, a force measurement assembly configured to receive a subject, at least one visual display device, and one or more data processing devices operatively coupled to the user input device, the force measurement assembly, and the visual display device. The one or more data processing devices are programmed to receive the one or more input signals from the at least one user input device based upon one or more selections by the user, the one or more selections by the user including a scene parameter for one or more scenes on the output screen of the at least one visual display device; and display the one or more scenes on the output screen of the at least one visual display device based upon the scene parameter selected by the user.

Force measurement system

A system for estimating a pose of one or more persons in a scene includes a camera configured to capture one or more images of the scene; and a data processor configured to execute computer executable instructions for: (i) receiving the one or more images of the scene from the camera; (ii) extracting features from the one or more images of the scene for providing inputs to a keypoint subnet and a person detection subnet; (iii) generating one or more keypoints using the keypoint subnet; (iv) generating one or more person instances using the person detection subnet; (v) assigning the one or more keypoints to the one or more person instances by learning pose structures from image data; and (vi) determining one or more poses of the one or more persons in the scene using the assignment of the one or more keypoints to the one or more person instances.

System for estimating a pose of one or more persons in a scene

A head-mounted display system is disclosed herein. In one or more embodiments, the head-mounted display system includes a head-mounted visual display device and an instrumented floor with one or more instrumented floor sections configured to detect movements, forces, and/or moments for a user and generate one or more output signals based upon the detected movements, forces, and/or moments for the user; and at least one data processing device operatively coupled to the head-mounted visual display device and the instrumented floor. The at least one data processing device being programmed to receive the one or more output signals from the instrumented floor; and determine one or more movements, output forces, and/or output moments for the user based upon the one or more output signals from the instrumented floor. The instrumented floor enables a plurality of different movement patterns and/or protocols to be executed thereon by the user.

A body sway measurement system is disclosed herein. The body sway measurement system includes an inertial measurement unit or camera configured to generate time series output data for determining one or more parameters indicative of the body sway of a user; and a mobile device or other computing device having a data processor, the data processor including at least one hardware component, the data processor being operatively coupled to the inertial measurement unit or camera, the data processor configured to receive the time series output data from the inertial measurement unit or camera, and the data processor and/or a cloud server configured to determine the one or more parameters indicative of the body sway of the user using a trained neural network.

Body sway measurement system

A system for training neural networks that predict the parameters of a human mesh model is disclosed herein. The system includes at least one camera and a data processor configured to execute computer executable instructions for: receiving a first frame and a second frame of a video from the at least one camera; extracting first and second features from the first and second frames of the video; inputting the sequence of frames of the video into a human mesh estimator module, the human mesh estimator module estimating mesh parameters from the sequence of frames of the video so as to determine a predicted mesh; and generating a training signal for input into the human mesh estimator module by using at least one of: (i) a depth loss module and (ii) a rigid transform loss module.

A swing analysis system is disclosed herein. The swing analysis system includes a motion capture system comprising at least one motion capture device configured to detect motion of one or more body segments of a user and generate first output data, and further configured to detect the motion of a head and/or face of the user, a hand and/or fingers of the user, and/or an object being manipulated by the user, and generate second output data; and at least one data processing device operatively coupled to the motion capture system, the at least one data processing device configured to determine one or more swing performance parameters for the user using first positional information of the one or more body segments of the user, and/or second positional information for the head and/or face of the user, the hand and/or fingers of the user, and/or the object being manipulated by the user.

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a first branch pose estimation neural network and a second branch pose estimation neural network; (iii) generating, by using a three dimensional reconstruction module, three dimensional reconstructions from two dimensional pose estimates produced by the second branch pose estimation neural network; and (iv) projecting, by using a reprojection module, the three dimensional reconstructions to camera image planes of respective image samples, and uploading the reprojections and image samples to an annotation server.

Growing community of inventors

Columbus, OH, United States of America

Mohan Chandra Baro