A golfer-oriented information providing method includes steps of inputting needs information regarding a golf swing to a computer, inputting swing attention points and advice information with an information provision priority, inputting reference golf swing information, importing first needs information of a first golfer, inputting first golf swing information of the first golfer, retrieving first reference golf swing information based on the first needs information by the computer, extracting a difference between the first reference golf swing information and the first golf swing information regarding the swing attention points of the first needs information, and selecting the advice information based on a magnitude of the difference and the information provision priority and providing the selected advice information to the first golfer.

The present disclosure provides a design for an instant multifunctional golf practicing tent with one or more outer receiving nets for achieving a significantly larger golf ball receiving range with a smaller tent footprint, which improves adaptability and portability. It features a collapsible frame structure with an instant hub for easy assembling or disassembling the golf practicing tent; at least one receiver or receiving net located outside of the tent with a large receiving area covering the front of the tent; corner pockets are located at the base of the front opening for stopping the ball from bouncing back to hurt people and properties. Other embodiments include: an additional inner or outer receiving net for added safety; a tilted floor panel for easy ball retrieval; floor distance markings, net range maps, and a tracking device for tracking performance and simulating results in real golf courses; multiple functionalities as different shelter types.

A virtual training system and method are disclose. The system includes two-dimensional (2D) and three-dimensional (3D) image sensors, a biometric data sensor, a processor, and an output device. The 2D image sensor senses a 2D image of a user, and the 3D image sensor senses a 3D image of the user including a movement of the user. The biometric data sensor senses at least one biometric characteristic of the user. The processor generates feedback information in real time during a session relating to movement of the user compared to a standard form movement, which may be an exercise movement, a dance movement, a martial arts movement, or a physical therapy movement. Feedback is provided by the output device as an image of the user captured by at least one of the 2D image sensor and the 3D image sensor overlayed on an instructor avatar, which illustrates the standard form movement.

An example method of modeling a portion of a golf club and a golf swing includes scanning the golf club to obtain scanning information, training a convolutional neural network using the scanning information, using at least one camera to obtain a series of images, converting the series of images into parameterized motion representations, using at least one radar to obtain a radar signal, converting the radar signal into time-frequency images, inputting the parameterized motion representations and the time-frequency images into the convolutional neural network, receiving golf club parameters and golf swing parameters as an output of the convolutional neural network, and generating a visual model of the golf club and the golf swing in a virtual space using the golf club parameters and the golf swing parameters.

An exercise machine includes a cable. It further includes an interface to a moveable camera device coupled with the exercise machine. It further includes a processor configured to receive a cable-based measurement associated with an exercise performed by a user. The processor is further configured to receive, from the moveable camera device, video information associated with the exercise. The processor is further configured to provide a workout determination based at least in part on both the cable-based measurement and the video information received from the moveable camera device.

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.

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.

The disclosure relates to a system for evaluating movement of a body of a user. The system may include a video display, one or more digital cameras, and a processor. The processor may control the one or more cameras to generate images of at least the part of the body over a period of time. The processor may estimate a position of a plurality of joints of the body. The processor may receive a selection of a tracked pose, and determine, from the plurality of joints, a set of joints associated with the tracked pose. The processor may generate at least one joint vector connecting joints in the set of joints, and assign, based on changes in the joint vector over the period of time, a form score to a performance of the tracked pose. The processor may then generate a user interface that depicts the form score.

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.

Provided herein is a mechatronic simulator system that provides various types of unexpected perturbations to challenge the proactive and reactive balance control in subjects to thereby improve balance control of the subject. The system includes a motion capture unit and a processing unit capable of analyzing the subject’s response to the perturbations, provide real time feedback, adjust and/or determine training sessions.