During a first time period and for a first user, a second user is automatically selected based on competitive data of the first user and competitive data of the second user, and a workout selection is sent to cause a video of a workout to be displayed during a second time period on a smart mirror of the first user and a smart mirror of the second user. During the second time period, a live stream of the first user exercising is displayed at the smart mirror of the second user, and a live stream of the second user exercising is received and displayed at the smart mirror of the first user. During the second time period, a performance score of the first user and a performance score of the second user is displayed at the smart mirrors of the first user and the second user.

A method is disclosed for using an artificial intelligence engine to modify resistance of pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive measurements as input, and outputting, based on the measurements, a control instruction that causes the exercise device to modify, independently from each other, the resistance of the pedals. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the pedals. The method includes determining, based on the measurements, a quantifiable or qualitative modification to the resistance provided by a pedal of the pedals. The resistance provided by another pedal of the pedals is not modified. The method includes transmitting the control instruction to the exercise device to cause the resistance provided by the pedal to be modified.

A walking training system includes a treadmill configured to prompt a trainee to walk, a display device installed such that the trainee views the display device while walking on the treadmill, a camera configured to image the trainee at an angle of view at which a gait of the trainee is recognizable, a calculation unit configured to calculate a tilt of a body core of the walking trainee based on an image captured by the camera, and a display control unit configured to control the display device to display a body core line associated with the tilt, and an index indicating at least an end of a permissible range of a deflection of the body core line.

A timely component movement measuring system for a vehicle is disclosed. The system includes a component of a vehicle, the component having a range of motion. The system also includes a sensor to measure a movement of the component through some or all of the range of motion of the component.

A pedal drive system, in particular for an electric vehicle or a training apparatus, and for generating electrical power from muscle power of a user with at least one pedal and an electric generator, connected mechanically with said at least one pedal, is provided. To improve the haptic feel and feedback at the pedal, a control unit is provided for controlling a feedback torque, applied at said pedal, wherein the control unit comprises a haptic renderer, configured for control of said feedback torque based on at least one user-defined pedal reference trajectory.

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 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.

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.