System and method for using drag force data to optimize athletic performance

A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:

• receive first image data, wherein the first image data includes first pixel data associated with the user performing the exercise at a first time;
• receive second image data, wherein the second image data includes second pixel data associated with the user performing the exercise at a second time different than the first time;
• determine, based on a difference between the first pixel data and the second pixel data, deviation data associated with a profile of the user;
• generate outline data based on the deviation data and corresponding to a frontal area of the user;
• determine drag coefficient data based on the frontal area of the user;
• generate, via an artificial intelligence engine, a machine learning model trained to determine depth profile data corresponding to a side profile of the user;
• update, based on the depth profile data, the drag coefficient data;
• and
• determine, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.

A system for optimizing at least one exercise performed by a user using an exercise device, the system comprising:

• a user interface;
• a memory device storing instructions;
• a processing device communicatively coupled to the memory device, the processing device executes the instructions to:
• receive first image data, wherein the first image data includes first pixel data associated with the user performing the exercise at a first time;
• receive second image data, wherein the second image data includes second pixel data associated with the user performing the exercise at a second time different than the first time;
• determine, based on a difference between the first pixel data and the second pixel data, deviation data associated with a profile of the user;
• generate outline data based on the deviation data and corresponding to a frontal area of the user;
• determine drag coefficient data based on the frontal area of the user;
• generate, via an artificial intelligence engine, a machine learning model trained to determine depth profile data corresponding to a side profile of the user;
• update, based on the depth profile data, the drag coefficient data;
• and
• determine, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.

An apparatus comprising:

• a memory device storing instructions;
• a processing device communicatively coupled to the memory device, the processing device executes the instructions to:
• receive first image data, wherein the first image data includes first pixel data associated with the user performing the exercise at a first time;
• receive second image data, wherein the second image data includes second pixel data associated with the user performing the exercise at a second time different than the first time;
• determine, based on a difference between the first pixel data and the second pixel data, deviation data associated with a profile of the user;
• generate outline data based on the deviation data and corresponding to a frontal area of the user;
• determine drag coefficient data based on the frontal area of the user;
• generate, via an artificial intelligence engine, a machine learning model trained to determine depth profile data corresponding to a side profile of the user;
• update, based on the depth profile data, the drag coefficient data;
• and
• determine, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.