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

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.

What is claimed is: 
     
         1 . A method for optimizing at least one exercise performed by a user using an exercise device, the method comprising:
 receiving first image data, wherein the first image data includes first pixel data associated with the user performing the exercise at a first time;   receiving 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;   determining, based on a difference between the first pixel data and the second pixel data, deviation data associated with a profile of the user;   generating outline data based on the deviation data and corresponding to a frontal area of the user;   determining drag coefficient data based on the frontal area of the user; and   determining, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.   
     
     
         2 . The method of  claim 1 , further comprising controlling the exercise device based on the drag force data. 
     
     
         3 . The method of  claim 2 , wherein controlling the exercise device includes modifying a parameter of at least a portion of the exercise device, and wherein the parameter comprises at least one of a resistance, a speed, a time, a weight, a force, a pressure, a movement speed of a portion of the exercise device, a movement acceleration of a portion of the exercise device, a movement jerk of a portion of the exercise device, and a torque level of a portion of the exercise device. 
     
     
         4 . The method of  claim 1 , further comprising generating, via an artificial intelligence engine, a machine learning model trained to determine depth profile data corresponding to a side profile of the user. 
     
     
         5 . The method of  claim 4 , further comprising updating, based on the depth profile data, the drag coefficient data. 
     
     
         6 . The method of  claim 1 , further comprising determining, based on the drag force data, at least one of simulated speed data and simulated energy data; and
 presenting, on a user interface associated with the exercise, at least one of the simulated speed data and simulated energy data while the user uses the exercise device.   
     
     
         7 . The method of  claim 6 , further comprising determining, based on at least one attribute of the user, ideal position data, wherein the at least one attribute includes at least one of an age, a weight, a gender, a height, a body mass index, and a medical condition. 
     
     
         8 . The method of  claim 7 , further comprising determining deviation data based on a difference between the outline data and the ideal position data; and presenting, on the user interface, at least one of the ideal position data and the deviation data. 
     
     
         9 . The method of  claim 1 , wherein the image data is generated via an imaging device positioned adjacent a front of the exercise device. 
     
     
         10 . The method of  claim 9 , further comprising generating, via an artificial intelligence engine, a machine learning model trained to:
 determine the drag coefficient data based on the frontal area of the user,   determine, based on the outline data and the drag coefficient data, the drag force data, or both.   
     
     
         11 . The method of  claim 1 , further comprising:
 generating a target position of the user, the target position corresponding to an aerodynamic position of the user using the exercise device to perform the exercise;   monitoring an actual position of the user using the exercise device to perform the exercise;   calculating differential data based on a difference between the actual position of the user and the target position of the user; and   transmitting the differential data to the user while the user performs the exercise.   
     
     
         12 . The method of  claim 11 , further comprising presenting, on a user interface, the differential data concurrently with an instruction guiding the actual position of the user to the target position of the user. 
     
     
         13 . 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; and   determine, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.   
     
     
         14 . The computer-readable medium of  claim 13 , wherein the processing device is further to control the exercise device based on the drag force data. 
     
     
         15 . The computer-readable medium of  claim 13 , wherein the processing device is further to generate, via the artificial intelligence engine, a machine learning model trained to determine depth profile data corresponding to a side profile of the user. 
     
     
         16 . The computer-readable medium of  claim 15 , wherein the processing device is further to update, based on the depth profile data, the drag coefficient data. 
     
     
         17 . The computer-readable medium of  claim 13 , wherein the image data is generated via an imaging device positioned adjacent a front of the exercise device. 
     
     
         18 . The computer-readable medium of  claim 13 , wherein the processing device is further to:
 generate a target position of the user, the target position corresponding to an aerodynamic position of the user using the exercise device to perform the exercise;   monitor an actual position of the user using the exercise device to perform the exercise;   calculate differential data based on a difference between the actual position of the user and the target position of the user; and   transmit the differential data to the user while the user performs the exercise.   
     
     
         19 . The computer-readable medium of  claim 18 , wherein the processing device is further to:
 present, on a user interface, the differential data concurrently with an instruction guiding the actual position of the user to the target position of the user.   
     
     
         20 . A system for optimizing at least one exercise performed by a user using an exercise device, the system 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; and 
 determine, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.