Systems and methods for sensing and monitoring various athletic performance metrics, e.g., during the course of a game, a practice, a training session, training drills, and the like, are described. These systems and methods can provide useful metrics for players and coaches relating to athletic performances in various sports, including various team sports.

An adjustable weight exercise system is provided. A permanent weight assembly and a plurality of interlocking weights are provided. A base comprises an interlocking weight carrier, wherein the interlocking weights are supported on the interlocking weight carrier and an interlocking weight selector, wherein the interlocking weight selector is adapted to cause interlocking weights to be set to be locked to the permanent weight assembly while the permanent weight assembly is not on the base, wherein placement of the permanent weight assembly on the base causes interlocking weights that are set to be locked to the permanent weight assembly to be locked to the permanent weight assembly, and wherein placement of the permanent weight assembly on the base does not cause interlocking weights that are not set to be locked to the permanent weight assembly to be locked to the permanent weight assembly.

A display method executed by a processor includes: acquiring an identification result relating to any one of a plurality of elements included in a series of exercise and a position of an athlete that performs the elements based on 3D sensing data that is acquired by sensing the series of exercise by the athlete of a sport scored by a judge and an element dictionary data in which characteristics of an element and a position of the sport scored by a judge are defined; identifying any one of an element and a position that is being displayed in a 3D model video based on the 3D sensing data corresponding to the series of exercise, based on the identification result; and identifying, for the any one of the element and the position being displayed, time information corresponding to a boundary with any one of a next element and a next position.

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 method and system for measuring exercise data using a plurality of wireless sensors is disclosed. The plurality of wireless sensors are attached onto or into a plurality of pieces of strength training equipment, wherein each individual wireless sensor from the plurality of wireless sensors is attached to and associated with an individual piece of strength training equipment of the plurality of pieces of strength training equipment. An individual piece of strength training equipment of the plurality of pieces of strength training equipment is selected and the wireless sensor is registered with selected piece of strength training equipment with a computing device. Strength training equipment details may be identified through the computing device.

Fitness tracking devices and methods of operating the same. The fitness tracking device includes a sensor circuit to generate sensor data; a processor coupled to the sensor circuit; and a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to: buffer sensor data associated with motion of the user limb; generate an exercise prediction based on a prediction model and the sensor data, the prediction model defined by one or more oscillating signal profiles to identify genus predictions for respective limb movement types about at least one sensor axis, wherein the exercise prediction is generated based on a combination of an identified genus prediction associated with the generated sensor data and environment data associated with motion of the user limb; and transmit a signal representing the exercise prediction for display on a user interface.

Disclosed is an exercise posture correction assistance method using working muscle information depending on motion performed by at least one server, including acquiring first image information containing movement of a user corresponding to a specific practice motion from a user terminal, deriving skeleton information corresponding to the movement of the user based on the first image information, generating second image information by indicating the movement of the user using a skeleton shape based on the first image information and the skeleton information, deriving muscle information corresponding to the specific practice motion from a database, generating muscle movement image information corresponding to the movement of the user based on the skeleton information and the muscle information, and providing the second image information and the muscle movement image information to the user terminal.

An apparatus having processing circuitry configured to receive a selection of a predetermined activity, receive baseline data, and continuously perform tracking of user progress with respect to the predetermined activity as a function of the baseline data. The processing circuitry generates tracking data based on the tracking, continuously generates progress change data as a function of the tracking data, and outputs the progress change data to an external device. The progress change data is processed by the external device to output the progress change data to a display screen as a Graphical User Interface (GUI), a color scheme of the GUI being continuously updated as a function of being redisplayed based on the continuously generated progress change data.

Electromechanical rehabilitation of a user can include receiving a pedal force value from a pedal sensor of a pedal; receiving a pedal rotational position; based on the pedal rotational position over a period of time, calculating a pedal velocity; and based at least upon the pedal force value, a set pedal resistance value, and the pedal velocity, outputting one or more control signals causing an electric motor to provide a driving force to control simulated rotational inertia applied to the pedal.

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.