Sensor data is collected from a sensor monitoring a tension generating device of an exercise machine. The sensor data is analyzed to determine whether the sensor data is within a specification. In the event the sensor data is not within a specification, an error stop mode is entered for the tension generating device.

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.

A walking training system according to the present embodiment includes: a treadmill; a load distribution sensor that is provided on a lower side of a belt of the treadmill so as not to move together with the belt and that detects a distribution of a load received from a sole of a trainee riding on the belt of the treadmill; an extraction unit that extracts a load distribution in a region corresponding to a position of the sole of the trainee during walking training, out of a load distribution detected by the load distribution sensor; and a determination unit that determines a walking state of the trainee based on the load distribution extracted by the extraction unit.

The present disclosure provides a method comprising the steps: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan including an exercise; receiving first measurement data, where the first measurement data is associated with a first performance data; receiving second measurement data, where the second measurement data is associated with at least one of a second performance data and a second patient identifier; one of anonymiztion of and pseudonymization of the second patient identifier; determining, via an artificial intelligence engine and based on one or more differences between the first and the second measurement data, differential data; and presenting, with a patient interface, at least one of the differential data, first measurement data, second measurement data, and the first patient data.

A method for updating a treatment plan. The treatment plan is associated with a user using a treatment apparatus to perform the treatment plan. The method includes receiving first data associated with a first diagnosis of the user. The method includes generating, based on the first data, an initial treatment plan to be performed on the treatment apparatus by the user. The method includes receiving second data associated with a first attribute of the user. The method includes generating, via an artificial intelligence engine, a machine learning model trained to generate an updated treatment plan based on the initial treatment plan and the second data.

A system for ankle rehabilitation includes a motorized platform arranged to hold an ankle of a subject to be rehabilitated; a first sensor module arranged to detect signals representing movement intention of the ankle on the motorized platform; a second sensor module arranged to detect signals representing actual movement of the ankle on the motorized platform; and a processor arranged to process the signals detected by the first sensor module and the signals detected by the second sensor module, for control of movement of the motorized platform.

The present disclosure relates to a stationary exercise apparatus for indoor cycle training (1), i.e. a stationary exercise bicycle, preferably provided with a magnetic resistance unit (15). One embodiment relates to a stationary exercise bike comprising a flywheel (10) defining a radial gap (25) in the periphery wherein at least the periphery of the flywheel (10) has ferromagnetic properties, and wherein a magnetic resistance unit (15) is configured to controllably insert one, two or more magnets into said radial gap.

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.

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.