A method for exercising muscles in an ankle, foot, and/or leg of a user includes positioning a foot of a user onto a first body of an exercise device. The first body is spaced away from a second body of the device and pivotably connected to the second body of the device at a pivot axis. The pivot axis is adjacent to a central portion of the first body. The method includes rotating the first body about the pivot axis, with the foot, against a first resistive force. Rotating the first body includes subjecting the foot to a first motion. The method includes positioning the foot onto the second body. The method includes rotating the second body about the pivot axis, with the foot, against a second resistive force. Rotating the second body includes subjecting the foot to a second motion, the second motion being different than the first motion.

A portable exercise device includes a pedal spaced away from and pivotably connected to a base and having a neutral position relative to a pivot axis. The pedal is configured to rotate about the pivot axis in a first direction toward the base and in a second direction, opposite the first direction, toward the base. The device also includes a resistance mechanism configured to exert a force on the pedal about the pivot axis in a direction opposite to the respective direction of rotation of the pedal. The device is movable between an open, in-use configuration, where the pedal is disposed in the neutral position to receive a foot and spaced away from the base, and a closed configuration, where the pedal is adjacent the base.

A method is disclosed for using an artificial intelligence engine to interact with a user of an exercise device during an exercise session. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive data as input, and based on the data, providing an output. While a user performs an exercise using the exercise device, the method includes receiving the data from an input peripheral of a computing device associated with the user. Based on the data being received from the input peripheral, the method includes determining, via the machine learning model, the output to control an aspect of the exercise device.

A method is disclosed for using an artificial intelligence engine to perform a control action. The control action is based on one or more measurements from a wearable device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive the one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the control action to be performed. The method includes receiving the one or more measurements from the wearable device being worn by a user, determining whether the one or more measurements indicate, during an interval training session, that one or more characteristics of the user are within a desired target zone, and responsive to determining that the one or more measurements indicate the one or more characteristics of the user are not within the desired target zone during the interval training session, performing the control action.

A method is disclosed for using an artificial intelligence engine to modify resistance of one or more pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the exercise device to modify the resistance of the one or more pedals. The method includes receiving the one or more measurements from a sensor associated with the one or more pedals of the exercise device, determining whether the one or more measurements satisfy a trigger condition, and responsive to determining that the one or more measurements satisfy the trigger condition, transmitting the control instruction to the exercise device.

The disclosure relates to a cable tensioning station for providing a defined resistance force for resistance training, including a weight (9), which is arranged movably above a pull element (7, 17) and on which the resistance force is at least dependent, the pull element (7, 17) being guided via at least one coupling roller (18), which is designed to transfer a linear motion of the pull element as a rotary motion to a generator (19) to generate electrical energy, characterised in that the coupling roller (18) is embodied as a freewheel with adjustable blocking effect.

A method is disclosed for using an artificial intelligence engine to interact with a user of an exercise device during an exercise session. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive data as input, and based on the data, providing an output. While a user performs an exercise using the exercise device, the method includes receiving the data from an input peripheral of a computing device associated with the user. Based on the data being received from the input peripheral, the method includes determining, via the machine learning model, the output to control an aspect of the exercise device.

An ankle exercise device comprises a base frame, a heel rest, a rotational component, and a plurality of resistance bands. The resistance bands may be connected to the rotational component. The rotational component rotates 90 degrees to orient the resistance bands in either a horizontal or vertical direction. The heel rest may comprise a u-shaped cutout to cup a user's heel. The heel rest is adjustable to align the heads of a user's metatarsals between the resistance bands. With the resistance bands in the horizontal direction, the user may rotate the ankle in the plantarflexion and dorsiflexion directions. With the resistance bands in the vertical direction, the user may rotate the ankle in the inversion and eversion directions.

A method is disclosed for using an artificial intelligence engine to modify resistance of pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive measurements as input, and outputting, based on the measurements, a control instruction that causes the exercise device to modify, independently from each other, the resistance of the pedals. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the pedals. The method includes determining, based on the measurements, a quantifiable or qualitative modification to the resistance provided by a pedal of the pedals. The resistance provided by another pedal of the pedals is not modified. The method includes transmitting the control instruction to the exercise device to cause the resistance provided by the pedal to be modified.

An exercise step and method of exercising a user's legs is disclosed. The exercise step includes a main body which has a substantially planar and horizontally oriented upper wall and a sloped wall extending from bottommost surface of the exercise step to the upper wall. In use, a user steps on the sloped wall with a heel portion of his or her foot and raises the heel portion such that a sole portion of his or her foot is supported by the upper wall.