Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 second branch pose estimation neural network; (iii) generating a first training signal from the second branch pose estimation neural network using a three dimensional reconstruction module for input into the first branch pose estimation neural network; (iv) generating one or more volumetric heatmaps; and (v) applying a maximization function to the one or more volumetric heatmaps to obtain a 3D pose of one or more persons in the scene.

Abstract: A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes at least one camera, the at least one camera configured to capture an image of the scene; and a data processor including at least one hardware component, the data processor configured to execute computer executable instructions. The computer executable instructions comprising instructions for: (i) receiving the image of the scene from the at least one camera; (ii) extracting features from the image of the scene for providing inputs to a convolutional neural network; (iii) generating one or more volumetric heatmaps using the convolutional neural network; and (iv) applying a maximization function to the one or more volumetric heatmaps to obtain a three dimensional pose of one or more persons in the scene.

Abstract: A measurement system includes a motion capture system comprising at least one motion capture device configured to detect the motion of one or more body portions of a system user and generate output data; a sensory output device, the sensory output device configured to generate sensory feedback for delivery to the system user; and at least one data processing device operatively coupled to the motion capture system and the sensory output device, the at least one data processing device further configured to determine, by utilizing a trained neural network, one or more poses and/or movements of the one or more body portions of the system user from the motion capture output data, and to provide sensory feedback to the system user, by means of the sensory output device, as to conformity of the one or more poses and/or movements to one or more reference poses and/or movements.

Abstract: A system for estimating a three dimensional pose and determining one or more biomechanical performance parameters of at least one person in a scene is disclosed herein. The system includes at least one camera and at least one measurement assembly, the at least one camera configured to capture an image of the scene; and a data processor including at least one hardware component, the data processor configured to execute computer executable instructions. The computer executable instructions comprising instructions for: (i) receiving the image of the scene from the at least one camera; (ii) extracting features from the image of the scene for providing inputs to a convolutional neural network; (iii) generating one or more volumetric heatmaps using the convolutional neural network; and (iv) applying a maximization function to the one or more volumetric heatmaps to obtain a three dimensional pose of the at least one person in the scene.

Abstract: A force measurement system is disclosed herein. The force measurement system includes a force measurement assembly configured to receive a subject thereon, and one or more data processing devices operatively coupled to the force measurement assembly. In one or more embodiments, the one or more data processing devices are operatively coupled to the force measurement assembly, the one or more data processing devices configured to receive one or more signals that are representative of forces and/or moments being applied to a top surface of the force measurement assembly by the subject, and to convert the one or more signals into output forces and/or moments, the one or more data processing devices further configured to predict one or more balance parameters of the subject using a trained neural network.

Abstract: A force measurement system is disclosed herein. In one embodiment, the system includes a force measurement assembly configured to receive a subject; a data processing device operatively coupled to the force measurement assembly, the data processing device configured to determine a center of pressure for the subject using output forces and/or moments from the force measurement assembly; and a mobile device configured to determine one or more parameters indicative of the body sway for the subject. In this embodiment, a fall risk of the subject is assessed based upon a combination of the center of pressure and the one or more parameters indicative of the body sway determined for the subject.

Abstract: A force measurement system is disclosed herein. The force measurement system includes a force measurement assembly configured to receive a subject thereon, and one or more data processing devices operatively coupled to the force measurement assembly. In one or more embodiments, the one or more data processing devices are configured to receive one or more signals that are representative of forces and/or moments being applied to the top surface of the force measurement assembly by the subject, and to convert the one or more signals into output forces and/or moments, and the one or more data processing devices are further configured to determine a type of balance strategy that the subject is using to maintain his or her balance on the force measurement assembly.

Abstract: A force measurement system is disclosed herein. In one embodiment, the system includes a force measurement assembly configured to receive a subject; a data processing device operatively coupled to the force measurement assembly, the data processing device configured to determine a center of pressure for the subject using output forces and/or moments from the force measurement assembly; and a mobile device configured to determine one or more parameters indicative of the body sway for the subject. In this embodiment, a fall risk of the subject is assessed based upon a combination of the center of pressure and the one or more parameters indicative of the body sway determined for the subject.