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 measurement and testing system is disclosed herein. The measurement and testing system includes at least one data processing device operatively coupled to at least one actuator, a visual display device, and a user input device. The at least one data processing device being configured to execute computer executable stored instructions for: (i) generating a graphical user interface with a protocol section comprising one or more protocols with one or more actuator parameters for controlling the at least one actuator for a time period; (ii) receiving an input signal from the user input device that initiates an execution of one of the one or more protocols for controlling the at least one actuator; and (iii) upon receiving the input signal from the user input device, controlling the at least one actuator for the time period in accordance with the executed one of the one or more protocols.

Abstract: 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 being programmed to receive the one or more input signals from the user input device based upon one or more selections by the user, the one or more selections including a scene movement setting and a force measurement assembly movement setting; display the one or more scenes on the output screen of the at least one visual display based upon the scene movement setting selected by the user; and control the force measurement assembly based upon the force measurement assembly movement setting selected by the user.

Abstract: A force measurement system is disclosed herein. The force measurement system includes a force measurement assembly with at least one first load transducer beam and at least one second load transducer beam, and a data processing device. The at least one first load transducer beam includes a first load transducer frame portion and a first plurality of deformation sensing elements disposed on the first load transducer frame portion. The at least one second load transducer beam includes a second load transducer frame portion and a second plurality of deformation sensing elements disposed on the second load transducer frame portion. The data processing device is operatively coupled to the first plurality of deformation sensing elements of the at least one first load transducer beam and the second plurality of deformation sensing elements of the at least one second load transducer beam.

Abstract: A cable actuation system configured to apply a force to a person is disclosed herein. The cable actuation system includes a cable having a first end and a second end, the second end being oppositely disposed relative to the first end, and the first end of the cable configured to be coupled to a person; an actuator operatively coupled to the second end of the cable, the actuator configured to apply a force to the cable; and a control system operatively coupled to the actuator, the control system configured to determine the force being applied to the cable by the actuator, and the control system configured to apply a controlled force to the person by means of the actuator based upon feedback from the force applied to the cable.

Abstract: A head-mounted display system is disclosed herein. The head-mounted display system includes an input device, the input device configured to output an input signal based upon an input response by a user; a head-mounted visual display device having an output screen, the head-mounted visual display device configured to display one or more screen images on the output screen; and at least one data processing device operatively coupled to the input device and the head-mounted visual display device. The at least one data processing device is programmed to generate and display at least one displaceable visual object and visual target on the output screen of the head-mounted visual display device; receive an input signal from the input device based upon an input response by the user; and control the movement of the displaceable visual object towards the visual target based upon the input signal received from the input device.

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 force and/or motion measurement system is disclosed herein. The force and/or motion measurement system includes a motion capture device comprising at least one camera configured to detect a motion of a person, the at least one camera being mounted in a floor of a room or in a top component of a force measurement assembly; and at least one data processing device operatively coupled to the at least one camera of the motion capture device, the at least one data processing device configured to determine a position and/or movement of one or more limbs of the person based upon output data from the at least one camera of the motion capture device.

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 force measurement assembly is disclosed herein. The force measurement assembly includes a top component, the top component having a top surface for receiving at least one portion of the body of the subject; a single force transducer supporting the top component, the single force transducer configured to sense one or more measured quantities and output one or more signals that are representative of forces and/or moments being applied to the top surface of the top component by the subject; and a base component disposed underneath the single force transducer, the base component configured to be disposed on a support surface.

Abstract: A force measurement system is disclosed herein. The force measurement system includes a force measurement assembly configured to receive a subject, and one or more data processing devices operatively coupled to the force measurement assembly. In one embodiment, the one or more data processing devices are configured to determine a difference between a center of pressure and a center of mass for the subject from output force and/or moment data, and to determine the fall risk of the subject based upon the difference between the center of pressure and the center of mass for the subject. In another embodiment, the one or more data processing devices are configured to determine one or more balance parameters of the subject from the output force and/or moment data while the subject is stepping onto the top surface of the force measurement assembly and/or stepping off the top surface of the force measurement assembly.

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 swing analysis system is disclosed herein. The swing analysis system includes a motion capture system and a force measurement assembly configured to receive a user; and at least one data processing device operatively coupled to the motion capture system and the force measurement assembly, the at least one data processing device configured to determine positional information for the one or more body segments of the user from the output data of the at least one motion capture device and output forces and/or moments from the force measurement assembly, the at least one data processing device further configured to determine one or more swing performance parameters for the user using at least one of: (i) the positional information of the one or more body segments of the user from the at least one motion capture device, and (ii) the output forces and/or moments from the force measurement assembly.

Abstract: A force measurement assembly is disclosed herein. The force measurement assembly includes a top component, the top component having a top surface for receiving at least one portion of the body of the subject; a single force transducer supporting the top component, the single force transducer configured to sense one or more measured quantities and output one or more signals that are representative of forces and/or moments being applied to the top surface of the top component by the subject; and a base component disposed underneath the single force transducer, the base component configured to be disposed on a support surface.

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 system for testing and/or training the vision of a user is disclosed herein. The system includes a motion sensing device, a visual display device having an output screen, and a data processing device operatively coupled to the motion sensing device and the visual display device. In one embodiment, the data processing device is programmed to generate and display a configuration of a visual object on the visual display device during the same cycle of rotational head displacement when the actual head velocity or speed for the user reaches or exceeds a prescribed threshold percentage of the target velocity or speed. In another embodiment, the data processing device is programmed to increase a value of a prescribed amplitude or period for the head rotational displacement of the user for a successive trial of a test or training routine when a peak head velocity or speed is determined to have increased.

Abstract: A system for estimating a pose of one or more persons in a scene includes a camera configured to capture an image of the scene; and a data processor configured to execute computer executable instructions for: (i) receiving the image of the scene from the camera; (ii) extracting features from the image 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 the 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.