An exercise machine may include a frame, a resistance mechanism supported by the frame, first and second pull cables supported by the frame and linked to the resistance mechanism, a power rack attached to the frame, first and second handles selectively attachable to the first and second pull cable, and first and second pulleys. The power rack includes upright posts configured to have barbell holders adjustably attached thereto such that the barbell holders are configured to be adjusted in various positions between highest positions and lowest positions on the upright posts. The pulleys are configured to selectively receive the pull cables to enable the pull cables to be selectively attached to a barbell to assist or hinder a user in lifting the barbell in a combined cable and free weight workout, the pull cables decoupled from the pulleys for cable workouts.

The present disclosure generally relates to managing user interfaces associated with physical activities. The user interface displays a scrollable list of affordances associated with physical activities. The affordances may contain physical activity and heart rate information. A user can launch a physical activity tracking function or an interface to change a workout metric using an affordance. Users can also control the music that is played in response to selecting a workout. Further, the interface can be used to compose a reply message to a received message that contains workout information. The interfaces can show a graph that includes heart data when the user's heart rate meets a heart rate alert criteria.

An electromechanical device for rehabilitation includes pedals coupled to radially-adjustable couplings, an electric motor coupled to the pedals via the radially-adjustable couplings, and a control system including a processing device operatively coupled to the electric motor. The processing device configured to, responsive to a first trigger condition occurring, control the electric motor to operate in a passive mode by independently driving the radially-adjustable couplings rotationally coupled to the pedals. The processing device also configured to, responsive to a second trigger condition occurring, control the electric motor to operate in an active-assisted mode by measuring revolutions per minute of the radially-adjustable couplings, and cause the electric motor to drive the radially-adjustable couplings when the measured revolutions per minute satisfy a threshold condition, and responsive to a third trigger condition occurring, control the electric motor to operate in a resistive mode by providing resistance to rotation of the radially-adjustable couplings.

A method for ball game training, the method comprising steps executed by at least one computer, the steps comprising: receiving image data of a player and a ball, the image data being captured using at least two cameras, using the received image data, tracking motion of the player and motion of the ball in three dimensions, based on the tracked motions, predicting a position of the player and a trajectory of the ball, and based on the predicted position and trajectory, generating a control command for at least one ball throwing machine.

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 tmining 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 tmining 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.

An artificial intelligence (AI) exercise guidance device includes: a personal maximum weight (PMW) receiver configured to receive a PMW.sub.input of a user; and a PMW correction portion configured to correct the PMW.sub.input to a PMW.sub.AI based on a user objectification index, wherein the user objectification index is a numerical value calculated based on exercise data obtained during a predetermined time period.

Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The method comprises receiving attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The method also comprises, while the user uses a treatment apparatus to perform a first treatment plan for the user, receiving measurement data associated with the user. The method further comprises generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The method also comprises transmitting, to a computing device, the second treatment plan for the user.

An electronic device may determine a target exercise mode of a wearable device, determine a value of a first control parameter based on the target exercise mode, generate sensing data based on raw sensing data obtained from the wearable device and the value of the first control parameter, generate a target state factor corresponding to a target type of the target exercise mode based on the sensing data, determine a value of a second control parameter based on the target exercise mode, determine a torque value corresponding to the target type of the target exercise mode based on the target state factor and the value of the second control parameter, and/or control the wearable device based on the torque value.

In general, a computing device disposed in a cardio exercise machine receives data related to a fitness level of a user. The computing device selects a workout program based at least in part on the data. The operations of the cardio exercise machine are controlled based at least in part on the workout program. The computing device generates visual feedback based on at least one of the user's operation of the cardio exercise machine and the selected workout program measured against performance criteria.

Computer implemented methods and systems for automated coaching and distribution of fitness plans is disclosed. The method includes receiving, by a processor, a fitness related information from one or more clients in response to an initial questionnaire. The fitness related information includes at least meet details, a fitness goal, a fitness history, an exercise ability and a payment list. The method includes creating, by the processor, a plurality of arrays in an electronic document for each client of the one or more clients. The plurality of arrays is configured to store the fitness related information. The method includes processing, by the processor, the fitness related information automatically to generate a fitness plan for each client based on the plurality of arrays, and a set of predefined rules. The method further includes distributing, by the processor, the fitness plan automatically through a personal communication to each client.