Abstract: A method for optimizing at least one exercise is provided. The method includes receiving user data. The user data includes biometric attribute data associated with a user of an exercise device. The method includes generating at least one interval, the at least one interval including at least duration data and target power data each associated with the exercise. The method includes during the at least one interval, receiving measurement data associated with at least one of the user and the exercise device. The method includes calculating, based on the measurement data and the user data, energy data associated with an expended energy of the user during the at least one interval, the energy data including quantity data and source information each associated with at least one energy source. The method includes generating, via an artificial intelligence engine, a machine learning model trained to identify the at least one energy source.

Abstract: A method provides for optimizing at least one exercise. The method includes receiving first image data. The first image data includes first pixel data associated with the user performing the exercise at a first time. The method includes receiving second image data. The second image data includes second pixel data associated with the user performing the exercise at a second time. The method includes determining, based on a difference between the first pixel data and the second pixel data, deviation data associated with a profile of the user. The method includes generating outline data based on the deviation data and corresponding to a frontal area of the user. The method may also include determining drag coefficient data based on the frontal area of the user. The method includes determining, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.

Abstract: A method provides for optimizing at least one exercise. The method includes receiving first image data. The first image data includes first pixel data associated with the user performing the exercise at a first time. The method includes receiving second image data. The second image data includes second pixel data associated with the user performing the exercise at a second time. The method includes determining, based on a difference between the first pixel data and the second pixel data, deviation data associated with a profile of the user. The method includes generating outline data based on the deviation data and corresponding to a frontal area of the user. The method may also include determining drag coefficient data based on the frontal area of the user. The method includes determining, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.