A plurality of motion sensors capable of capturing IR video, depth video, and RGB images. Periodically emitting IR pulses. Capturing IR video, depth video, and RGB images of a user. Generating a point cloud of the user. Generating a 3D model of the user including the point cloud of the user. Inputting the IR video, depth video, and RGB images into a machine learning model. Estimating a set of joints of the user in the 3D model. Tracking, based on the estimated set of joints of the user in the 3D model, the user's motion over a period of time. Determining a number of repetitions of an exercise performed by the user. Determining a feedback value. Calculating, based on the number of repetitions and the feedback value, a user exercise score. Providing, via a graphical user interface, the user exercise score and the feedback value to the user.

Among other things, a processor executes instructions to (a) during a current instance of an exercise activity having a predefined scope and being performed by a subject competitor on a machine, compute a first performance metric for the subject competitor using performance data from the subject competitor's performance of a lesser scope than the predefined scope of the current instance of the exercise activity, the first performance metric being normalized to reflect a hypothetical performance over the predefined scope, (b) receive performance data representing an historical performance during a previous instance of the exercise activity by at least one other competitor on a machine, and (c) present to the subject competitor comparative data based on the first performance metric and on a second performance metric for the historical performance by at least one other competitor that is based on the received performance data and that reflects performance of the at least one other competitor of a scope of the previous instance that is substantially the same as the predefined scope.

An exercise feedback system receives exercise data captured by client devices of users performing musculoskeletal exercises. The exercise feedback system may provide captured images to a client device of a physical trainer (PT) who remotely provides feedback on the users' exercise performances, for example, by labeling images as indicative of proper or improper musculoskeletal form. A PT may track multiple users using a central feed, which includes content displayed in an order based on ranking of users by a model. Additionally, the exercise feedback system may provide an augmented reality (AR) environment. For instance, an AR graphic indicating a target musculoskeletal form for an exercise is overlaid on a video feed displayed by a client device. Responsive to detecting that a user's form is aligned to the AR graphic, the exercise feedback system may notify the user and trigger the start of the exercise.

A training system operates to provide an online training system that includes an instructional portion, a performance portion and a results report. A trainer can create training modules and/or training programs and then launch a training program in which one or more users, utilizing a user system, can participate. A training program consists of one or more training modules. Each training module is configured to first run in a demonstration mode, in which the operator streams a demonstration of the physical activity required by the training module to one or more user systems. Users can thus view the demonstration of the physical activity on the user systems. The training module then enters a trial or trial mode in which each of the participants then engage in performing the physical activity. The user systems present a video/graphical display to show various performance criteria of the training module. The user systems also include a camera that is configured to monitor the activity of the user weighed against the performance criteria. Once the trial mode is completed, the data gathered by the user system cameras is then analyzed and evaluated and presented back to the participants.

Example embodiments may relate to a system, method, apparatus, and computer readable media configured for monitoring a user performing an exercise and generating a avatar of the user and a virtual shadow, wherein the virtual shadow illustrates proper form of the exercise. The example embodiments may further be configured for determining an amount of overlap between the virtual avatar and the virtual shadow, and generating a feedback score based on the amount of overlap.

A robotic training device, system and method duplicates and guides a golfer through a perfect golf swing in several modes. The exoskeleton-like robotis device, and system utilizes a training method that employs a combination of either pneumatic, electrics or Bowden cables with small motors and actuators, which embraces the golfer so he/she can experience the “feel” and body movements that make a perfect golf swing and learn to perform the motion without the training device. Similarly, the robot training device, system may assist patients in rehabilitation from injuries, stroke, chronic illness or other conditions involving muscular atrophy.

A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject.

There is disclosed a system for conducting multi-member group competitions involving physical exertion, the system includes a competition server that serves to designate a set of parameters for a competition involving at least two competing groups of individuals engaged in physical activity, the set of parameters defining a win condition requiring physical activity to be completed by at least two separate individuals within each group. The system further receives data representative of physical activity of multiple individuals in a first group, receives data representative of physical activity of multiple individuals in a second group, determines whether the win condition has been satisfied by the first group or the second group, and identifies a winner as the first group or the second group that completed the win condition.

A tackle training apparatus includes a padded body member and two padded elongate leg members in which each leg member has a first end for abutting the body member and a second end for contacting the ground. The two leg members are arranged in relation to the body member to extend from and to be resiliently movable relative to and/or separable from the body member, and in one configuration where the second end of each of the two leg members is placed on the ground, the two leg members can support the body member such that the apparatus is in a standing position, provides an effective training apparatus for improvements in collisions and especially tackles in rugby. Use of the apparatus facilitates improvement in point of contact, height of approach, grip technique and grip strength.

Implementations generally relate to determining golf swing characteristics. In some implementations, a method includes obtaining a video of a person performing an action. The method further includes determining from the video a plurality of points associated with the person, where the determining of the plurality of points is performed for each frame of the video. The method further includes determining a plurality of three-dimensional (3D) coordinates for each point of the plurality of points, where the determining of the 3D coordinates is performed for each frame of the video. The method further includes determining a movement of the plurality of points based at least in part on the 3D coordinates. The method further includes determining one or more movement errors based at least in part on the movement of the plurality of points.