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.

A stationary indoor “smart” training bicycle includes a unique combination of adjustable components to provide configurable dimensions to adjust the frame size of the indoor bicycle to properly fit a rider. A system is also provided to process a digital image of an outdoor bicycle and determine and translate dimensions and adjustments to the indoor bicycle to match one or more dimensions (lengths, angles, separations, etc.) of the outdoor bicycle.

An indoor, stationary, bicycle training device that provides advantages over conventional designs of exercise bicycles is provided. The stationary bicycle may include a tilting/pivoting mechanism to orient the indoor bicycle to simulate descending or climbing. The indoor bicycle may include flexible and resilient frame elements to support the indoor training device to move side-to-side under some riding situations thereby simulating the side-to-side swaying motion of an outdoor bicycle under the same riding situations. The indoor bicycle may include several combinations of frame adjustments to provide configurable dimensions of the indoor bicycle to adjust the frame to properly fit the rider, which may be adjusted based on corresponding dimensions of a user's outdoor bicycle. Still other aspects of the stationary bicycle device may aid in creating an “outdoor” feeling while using the device.

An indoor, stationary, bicycle training device that provides advantages over conventional designs of exercise bicycles is provided. The stationary bicycle may include a tilting/pivoting mechanism to orient the indoor bicycle to simulate descending or climbing. The indoor bicycle may include flexible and resilient frame elements to support the indoor training device to move side-to-side under some riding situations thereby simulating the side-to-side swaying motion of an outdoor bicycle under the same riding situations. The indoor bicycle may include several combinations of frame adjustments to provide configurable dimensions of the indoor bicycle to adjust the frame to properly fit the rider, which may be adjusted based on corresponding dimensions of a user's outdoor bicycle. Still other aspects of the stationary bicycle device may aid in creating an “outdoor” feeling while using the device.

A multifunctional arm cranking exerciser includes an omnidirectional support bracket assembly, which comprises a five-way bracket mounted to a top thereof and having a left side to which a left arm exercising device is mounted and a right side to which a right arm exercising device is mounted. Also mounted on the five-way bracket are a damping adjustment device and a quick release pin device. The quick release pin device allows for quick coupling and positioning of left and right arm cranks of the left and right arm exercising devices at an angular difference of 0 degree or 180 degrees and also allows for quick release and decoupling of the left and right arm exercising devices to be independent of each other. The damping adjustment device adjusts and regulates a level of damping for achieving an effect of efficient exercising.

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.

The present disclosure relates to a multi-track exercise fitness apparatus, including a main frame, and a hypocycloid movement mechanism. The hypocycloid movement mechanism includes an internal gear, an external gear and a force applying member; the internal gear is fixedly connected to the main frame, the external gear internally is engaged with the internal gear, the force applying member is rotatably connected with the external gear, and the force applying member applies force to drive the external gear to roll in the internal gear, such that a movement track of the force applying member forms a hypocycloid. The multi-track exercise fitness apparatus can realize the combination of multiple exercise postures, so that the user can have a more comprehensive exercise effect and a more interesting exercise during fitness.

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.