An exercise bike and a handlebar assembly are provided. The handlebar assembly includes: a rod-shaped handlebar; a support post; a first connecting component comprising a first recess, and further comprising a first tenon and a second tenon; a second connecting component comprising a first side, a second side away from the first side and a sidewall connecting the first side to the second side, wherein the second side of the second connecting component is provided with a second recess, and further provided with a first mortise and a second mortise. When the second recess is aligned with the first recess, a first through-hole is formed for the handlebar passing through, the first tenon is inserted in the first mortise, and the second tenon is inserted in the second mortise. The handlebar assembly can be easily assembled to the exercise bike.

In response to physical connection of an exercise accessory with a portion of an exercise machine, an identifier of the portion of the exercise machine is received. A signal including the identifier of the portion of the exercise machine is transmitted.

Systems and methods for electronically creating and modifying a fitness plan are disclosed. The method may include receiving electronic user data, collecting electronic fitness data, and displaying a suggestion for a fitness activity based on the electronic user data and the electronic fitness data.

Disclosed are biofeedback methods and devices suitable for providing biofeedback useful for helping a user control an own breathing, for example, to help in inducing deep breathing, and such biofeedback devices further comprising a dispenser for dispensing an inhalable substance.

Example embodiments may relate to a system, method, apparatus, and computer readable media configured for monitoring a user performing an athletic movement and/or exercise and generating a combinatory fitness athleticism score. The score may comprise a fitness sub-score and a fitness sub-score. The athletic movement may comprise a plurality of drills configured to test a plurality of fitness attributes and/or a plurality of athleticism attributes of the user. At least one of the plurality of fitness attributes may be selected from the group consisting of: endurance, flexibility, strength and combinations thereof; and at least one of the plurality of athleticism attributes may be selected from the group consisting of: speed, agility, reaction, power, balance, and combinations thereof.

An exercise device includes a base defining an inner volume and a top supported by the base, the top defining an aperture. The exercise device further includes a force sensor configured to measure force on the top and a motor disposed within the base and below the top, the motor including a cable extendable through the aperture. The exercise deice further includes a controller communicatively coupled to each of the force sensor and the motor. The controller is adapted to actuate the motor in response to forces applied to the top as measured by the force sensor. The controller may also actuate the motor in response to one or more additional parameters related to the speed or force with which the cable is manipulated (e.g., pulled by a user).

During a first time period and for a first user, a second user is automatically selected based on competitive data of the first user and competitive data of the second user, and a workout selection is sent to cause a video of a workout to be displayed during a second time period on a smart mirror of the first user and a smart mirror of the second user. During the second time period, a live stream of the first user exercising is displayed at the smart mirror of the second user, and a live stream of the second user exercising is received and displayed at the smart mirror of the first user. During the second time period, a performance score of the first user and a performance score of the second user is displayed at the smart mirrors of the first user and the second user.

A user-specific workout can be generated by adjusting transform parameters of a user-agnostic training profile to conform to a user boundary specifying a user's ability to perform work. The user-agnostic training profile can be defined in a training space, specifying a power curve in a time domain whereas the user boundary can be defined in a boundary space, specifying amounts of user-produced work in a time window size domain. A training boundary can be generated based on the user-agnostic training profile by determining the largest area under the user-agnostic training profile power curve for various time window sizes, where each largest area under the user-agnostic training profile power curve produces a data point for the training boundary for that window size domain value. The training boundary can be transformed based on the user boundary, causing corresponding transformations to the user-agnostics training profile, converting it to a user-specific workout.

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.

Systems and methods for prompting a user to perform an exercise and monitoring the exercise are provided. Multiple independent sensors or sensor systems may be used to calculate energy expenditure. Various criteria may be used to manually or automatically select the independent sensor or sensor system or combination that will be used with the energy expenditure calculations.