Systems and methods are disclosed for generating a 3D avatar and object fitting recommendations using a biomechanical analysis of observed actions with a focus on representing actions through computer-generated 3D avatars. Physical quantities of biomechanical actions can be measured from the observations, and the system can analyze these values, compare them to target or optimal values, and use the observations and known biomechanical capabilities to generate the 3D avatars and object fitting recommendations.

Performance information associated with a previous repetition of an exercise movement is received. Performance of one or more upcoming repetitions is predicted, based at least in part on the performance information associated with the previous repetition of the exercise movement. A failure classification of whether the one or more upcoming repetitions is associated with an occurrence of physical failure is performed, based at least in part on the predicted performance of the one or more upcoming repetitions of the exercise movement. A number of repetitions in reserve is determined, based at least in part on the failure classification.

A method of creating a scalable dynamic jointed skeleton (DJS) model for enhancing psychomotor leaning using augmented cognition methods realized by an artificial intelligence (AI) engine or image processor. The method involves extracting a DJS model from either live motion images of video files of an athlete, teacher, or expert to create a scalable reference model for using in training, whereby the AI engine extracts physical attributes of the subject including arm length, length, torso length as well as capturing successive movements of a motor skill such as swinging a gold club including position, stance, club position, swing velocity and acceleration, twisting, and more.

A system and method for generating a play prediction for a team is disclosed herein. A computing system retrieves trajectory data for a plurality of plays from a data store. The computing system generates a predictive model using a variational autoencoder and a neural network by generating one or more input data sets, learning, by the variational autoencoder, to generate a plurality of variants for each play of the plurality of plays, and learning, by the neural network, a team style corresponding to each play of the plurality of plays. The computing system receives trajectory data corresponding to a target play. The predictive model generates a likelihood of a target team executing the target play by determining a number of target variants that correspond to a target team identity of the target team.

The virtual play system is composed of a mobile terminal 2 worn by a player P, a virtual play display terminal 6 provided with a display unit 86, and a cloud server 4 capable of communicating with the mobile terminal 2 and the virtual play display terminal 6. The mobile terminal 2 includes a data analysis unit that transmits first measurement data obtained by measuring and quantifying an actual motion of player P during play and second measurement data obtained by measuring and quantifying the motion of player P during simulated play to the cloud server 4, and the cloud server 4 includes a virtual play data generation unit that calculates virtual play data based on the first measurement data and the second measurement data, and the virtual play display terminal 6 displays the virtual play data.

A method for portable drill instruction for a sport can include: capturing a live video of a player of the sport in a venue for the sport playing a ball launched from a ball machine during a training drill for the player conducted in the venue; determining in response to the live video whether or not the player satisfies a training goal of the training drill when playing the ball; and providing a real-time feedback to the player during the training drill that indicates to the player whether or not the training goal is satisfied.

A wearable device for tracking swim activities of a user is provided. The wearable device may include one or more sensors configured to generate sensor data, and based on the sensor data, the wearable device may determine swim metrics such as swim stroke count, swim stroke type, swim lap count, and swim speed. The determined swim metrics may be filtered based on one or more swim periods during which the user is likely to have been swimming. The wearable device may determine such swim periods based on the sensor data and/or the determined swim metrics.

An exercise assisting device and exercise assisting method are provided. The device performs the following operations: transmitting a first control signal, the first control signal is related to a motion demonstration video corresponding to an exercise course data; receiving a video stream of a user; analyzing the video stream to generate a motion recognition result corresponding to the exercise course data of the user, and determining whether a motion target value is achieved according to the motion recognition result and the exercise course data; and when the motion target value is not achieved, the motion recognition result and the motion target value are input into an expert suggestion model to generate a follow-up motion suggestion and to determine a follow-up motion demonstration video corresponding to the follow-up motion suggestion.

Systems and methods for delivering exercise content to a user of an exercise apparatus include a database storing exercise content, data associated with a content provider and data associated with an application provider, and a platform server operable to facilitate delivery of the exercise content to the exercise apparatus. The platform server delivers for display on the exercise apparatus archived exercise classes for selection by the user, receives from the exercise apparatus a selection, transmits data representing content of the selected archived exercise class, receives performance data from the exercise apparatus based on activity by the user, and generates archived performance data representing archived user performance parameters for a plurality of other users over at least the portion of the selected archived exercise class. Data is synchronized and updated for display by the exercise apparatus to simulate the user competing with at least some of the other users.

A portable smart hand grip according to an embodiment of the present disclosure includes a first housing, a guide part combined with the first housing and including a first receiving portion and a second receiving portion, a second housing facing the first housing and having a first pressing portion and a second pressing portion which are received in the first receiving portion and the second receiving portion, respectively, a first elastic member disposed between the first pressing portion and the first housing, a second elastic member disposed between the second pressing portion and the first housing, a sensor received in the first housing for detecting a user's operation of the portable smart hand grip, a control circuit unit received in the first housing to control an operation of the sensor, and a battery unit received in the first housing to provide power to the control circuit unit.