Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method is disclosed for using an artificial intelligence engine to present a user interface capable of presenting the progress of a user in domains. 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 user interface that causes icons to dynamically change position on the user interface. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the exercise device. The method includes presenting, on a computing device associated with the exercise device, sections of the user interface, wherein the sections are each related to a separate domain comprising the domains and wherein, based on the measurements, each section includes the icons placed.

Abstract: 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.

Abstract: 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.

Abstract: Systems, methods, and computer-readable mediums for generating, by an artificial intelligence engine, an exercise program comprising a first user energy score, wherein the method comprises generating, by the artificial intelligence engine, the exercise program including an exercise plan including a plurality of exercises. Each respective exercise is associated with user energy consumption metrics based at least on a metabolic equivalent of task (MET) value, and based on the user energy consumption metrics, the first user energy score is associated with the exercise program. The method includes receiving data pertaining to a plurality of users. The data includes physical activity goals the plurality of users desires to achieve. The method includes determining second user energy scores for the physical activity goals, and based on the first and second user energy scores, assigning, by the artificial intelligence engine, at least a subset of the plurality of users to the exercise program.

Abstract: Systems, methods, and computer-readable mediums for generating, by an artificial intelligence engine, an exercise plan for a user to perform. The method comprises receiving data pertaining to the user and generating user energy consumption metrics for a plurality of exercises. Each of the user energy consumption metrics is generated for a respective one of the plurality of exercises based at least on a metabolic equivalent of task (MET) value for the respective one of the plurality of exercises and user fitness test results. The method also includes generating the exercise plan based at least on the user energy consumption metrics and a user energy score. The exercise plan includes at least a subset of the plurality of exercises to be performed by the user to attempt to achieve the user energy score. The method further includes transmitting the exercise plan to a computing device.

Abstract: A method is disclosed for generating an improved exercise plan for a user to perform using at least an exercise machine. The method includes receiving data pertaining to the user. The data includes a physical activity goal the user desires to achieve and the physical activity goal includes levels of attainment to achieve. The method includes generating, by an artificial intelligence engine, the improved exercise plan. The improved exercise plan includes a set of exercises to be performed by the user to achieve the levels of attainment associated with the physical activity goal. The artificial intelligence engine uses at least one data source configured to include information pertaining to one or more exercises and at least one of the levels of attainment associated with the physical activity goal. The method includes transmitting the improved exercise plan to a computing device.

Abstract: A multidimensional exercise control system is disclosed herein. In one embodiment, a control system of an exercise machine comprises a memory device storing instructions, a user interface for presenting a video game with an avatar, and a processing device operatively coupled to the memory device and the user interface. The processing device is configured to execute the instructions to receive sensor data from a sensor operatively coupled to an exercise machine, wherein the sensor data comprises one or more measurements; responsive to the one or more measurements, cause the avatar to change a position in the video game; compare the one or more measurements to a target threshold; determine whether the one or more measurement exceed the target threshold; and responsive to determining that the one or more measurements exceed the target threshold, cause the user interface to present a modification to the video game.

Abstract: A method for generating an exercise session for a user using an exercise machine is disclosed herein. The method includes receiving a plurality of inputs, wherein the plurality of inputs comprise an indication of a level of pain of the user and a range of motion of a body part of the user. The method also includes determining, based on the plurality of inputs, an exercise level of the user, generating, using a machine learning model, the exercise session for the user by selecting, based on the exercise level of the user, one or more exercises to be performed by the user using an exercise machine, and causing initiation of the exercise session on the exercise machine and a virtual coach executed by a computing device associated with the exercise machine to provide instructions pertaining to the exercise session.

Abstract: 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.

Abstract: 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.

Abstract: A method is disclosed for using an artificial intelligence engine to onboard a user for an exercise plan. The method includes generating a machine learning model trained to receive as input onboarding data associated with a user and an onboarding protocol and, based on the onboarding data and the onboarding protocol, output an exercise plan. While a user performs an exercise using the exercise device, the method includes receiving the onboarding data associated with the user. The method includes determining, by the machine learning model using the onboarding data and the onboarding protocol, a fitness level of the user. The onboarding protocol includes exercises with tiered difficulty levels, the onboarding protocol increases a difficulty level for a subsequent exercise when an exercise is completed, and based on a completion state of a last exercise performed, the fitness level is determined. The method includes selecting a difficulty level for each exercise.

Abstract: 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.

Abstract: A method is disclosed for using an artificial intelligence engine to present a user interface capable of presenting the progress of a user in domains. 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 user interface that causes icons to dynamically change position on the user interface. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the exercise device. The method includes presenting, on a computing device associated with the exercise device, sections of the user interface, wherein the sections are each related to a separate domain comprising the domains and wherein, based on the measurements, each section includes the icons placed.

Abstract: 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.

Abstract: 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.