Computing device enhanced training environment system comprising a computing device, I/O subsystem for permitting a user to enter at least one attribute of the training or of the trainee, a plurality of sensors for generating sensory information, a training environment in which a training activity takes place, a database containing training related information. The computing device enhanced training environment system configured for at least one of the following: design a training program for a plurality of users, monitor training program performance, monitor training performance, instruct a user about the training, determine and/or set difficulty level in training apparatus.

A platform models and correlates physical activities based on users' interactions with a simple grip-metaphor design, enabling multi-dimensions actionable information to improve the health, performance and well-being of connected grip users within like-minded communities. For example, the platform captures multi-dimensional datasets generated from activities of each of a plurality of users on the online human internet of thing platform, where the activities include physical interactions with connected grips systems connected to the online human internet of thing platform. The platform then filters the captured multi-dimensional datasets into a plurality of categories and scores the filtered multi-dimensional data by the human internet of thing platform. Finally, the platform generates a multi-dimensional information modeling for each user based on the scored multi-dimensional data.

A method for displaying archived exercise classes comprising displaying information about archived exercise classes that can be accessed by a first user via a computer network on a display screen at a first location, wherein the first user can select among a plurality of archived classes, outputting digital video and audio content comprising the selected archived class, detecting a performance parameter for the first user at a particular point in the selected class, displaying the performance parameter on the display screen, and displaying performance parameters from a second user at a second location on the display screen such that at least one of the performance parameters from the first user and at least one of the performance parameters from the second user at the same point in the class are presented for comparison.

A method includes providing a first video file to a plurality of exercise machines, the first video file including content associated with an exercise class. The method also includes receiving user data from the plurality of exercise machines, the user data including respective settings associated with a common performance metric. In such a method, the respective settings are used on the plurality of exercise machines during playback of a particular part of the first video file. The method also includes identifying a timestamp associated with the particular part of the first video file, and generating an executable control corresponding to the performance metric. The method further includes generating a second video file comprising the content and the executable control. In such methods, playback of the second video file causes display of the executable control at a part of the second video file corresponding to the timestamp.

Systems and methods for providing a distortion-free pool-surface display beneath a swimmer. In one embodiment, projectors are positioned below a water level at opposite walls of a pool, and are configured to project images to a display surface at the bottom of the pool beneath a swimmer. A camera is used to view images projected by the projectors and to provide corresponding image data to an image correction platform on a graphics processing unit (GPU). The image correction platform identifies distortions in the images, generates image corrections that counter the identified distortions, and applies the corrections to subsequently projected images. The image correction platform also generates image adjustment that cause overlapping portions of the projected images to match seamlessly. The projected images may provide immersive experiences, coaching/training interfaces or other interactive displays.

Systems and methods are for production, management, syndication and distribution of digital assets through a network such as the Internet or wireless network. Digital media assets are distributed to consumers through a syndicated network of Outlets under control of a central platform. Distribution is format agnostic. There is a single platform between the B2B2C, wherein a platform allows, through a single access for each stakeholder, the ability to each have control over availability dates and pricing specifications to a plurality of merchants and/or subscription outlets or channels. Subscription channels can support micro-subscriptions with diversely curated and priced offerings, including acceptance of crypto-currency. The delivered media assets may include content associated with an exercise apparatus, including content for an exercise session including computer generated content and computer augmented content created in response to exercise session data.

An exercise recommendation system determines recommended weights for users to perform exercises with. The exercise recommendation system accesses a plurality of exercise pairs, each labeled with performance statistics of users who performed the exercises. Each exercise in an exercise pair is associated with a weight. The exercise recommendation system trains a machine learning model on the plurality of exercise pairs to determine a weight to recommend to a user for a first exercise based on performance statistics of the user associated with one or more second exercises, which are each in an exercise pair with the first exercise. The exercise recommendation system retrieves performance statistics of a target user including weights for exercises previously performed by the target user. The exercise recommendation system applies the machine learning model to the performance statistics to determine a target weight to recommend and modifies a user interface to include the target weight.

A smart ball-machine uses artificial intelligence to train a player or to play with a player. For example, the ball-machine can adjust the tennis ball speed, topspin, bounce according to the player's successful ball return rate. The ball-machine can be preconfigured with a profile of a player. For example, the ball-machine may download a complete profile of a tennis player from a game recording, or may download a file with a customized profile of a player to train a player using the ball-machine. The ball-machine is equipped with a plurality of wheels, motors, and shafts to provide a fully customizable launch of one or more balls. For example, the ball can be launched from the machine from one side of a tennis court to another side of a tennis court with a variety of speeds, trajectories, topspin, bounce etc.

A smart ball-machine uses artificial intelligence to train a player or to play with a player. For example, the ball-machine can adjust the tennis ball speed, topspin, bounce according to the player's successful ball return rate. The ball-machine can be preconfigured with a profile of a player. For example, the ball-machine may download a complete profile of a tennis player from a game recording, or may download a file with a customized profile of a player to train a player using the ball-machine. The ball-machine is equipped with a plurality of wheels, motors, and shafts to provide a fully customizable launch of one or more balls. For example, the ball can be launched from the machine from one side of a tennis court to another side of a tennis court with a variety of speeds, trajectories, topspin, bounce etc.

In response to an indication of a transition in a live workout, a synchronization identifier as metadata is embedded in a portion of a video stream of the live workout. The portion of the video stream of the live workout is transmitted to a remote client exercise machine. A hardware state of the remote client exercise machine is updated based at least in part on the synchronization identifier, at least in part to synchronize the remote client exercise machine with the live workout.

In response to an exercise machine joining a video stream of a live workout, a timeline comprising a set of events that are to occur over the course of the live workout is received. A portion of the video stream of the live workout is received, wherein the portion of the video stream has embedded a synchronization identifier. The portion of the video stream is rendered. A hardware state of the exercise machine is updated at least in part by evaluating the timeline using the synchronization identifier embedded in the portion of the video stream, at least in part to synchronize the exercise machine with the live workout.

An occurrence of a workout event with respect to one or more participants of a live workout is received. A prioritization of the event is determined based at least in part on at least one of a recency of the event or a categorization of the event. The event and the corresponding prioritization to a client exercise machine is transmitted, wherein a position of a presentation of the event on a feed of events on the client exercise machine is based at least in part on the prioritization of the event.