Software Platform Configured to Provide Analytics and Recommendations

Abstract

Methods and systems provide a software platform configured to provide analytics and recommendations. The software platform provides a software application that targets functional fitness with features that motivate and attract athletes for long-term use. The software application provides social networking functionalities including groups (e.g., gym, professionals, friends) as well as workout discovery, scalability, and fitness ranking. The software application further provides an engine that presents a workout generator based on available equipment and the user profile. The application includes an analysis tool that determines workout effectiveness including muscle groups, power measurements, equipment and movements, and effectiveness of workouts over time. In some embodiments, the application includes guidance on movements and stretches using instructions, videos, photos, etc.

What is claimed is: 
     
         1 . A method of forming a workout, the method implemented by one or more processors of a fitness genome apparatus communicatively coupled with a server and according to a set of instructions stored on a memory of the computing device, the method comprising:
 identifying, by the fitness genome apparatus, a movement associated with a user;   determining, by the fitness genome apparatus, a movement repetition count associated with the movement;   identifying, by the fitness genome apparatus, a group of movements, wherein the group of movements includes a plurality of movements and a plurality of movement repetition counts corresponding to each of the plurality of movements;   determining, by the fitness genome apparatus, a group repetition count for the group of movements;   forming, by the fitness genome apparatus, a workout based on the group of movements and the group repetition count; and   modifying, by the fitness genome apparatus, the workout based on performance data received from use of the workout.   
     
     
         2 . The method of  claim 1 , wherein identifying the movement comprises:
 receiving, by the fitness genome apparatus, physical characteristic data associated with the user;   detecting, by the fitness genome apparatus, a motion associated with the user; and   identifying, by fitness genome apparatus, the movement based on at least one of the physical characteristic data and the motion associated with the user.   
     
     
         3 . The method of  claim 1 , wherein determining the group repetition count is based on at least one of experience data associated with the user, a training regime selected by the user, and historical workout data associated with the user. 
     
     
         4 . The method of  claim 1 , comprising
 receiving, by the fitness genome apparatus and from the server, social media data comprising a list of people the user follows on a social media group; and   identifying, by the fitness genome apparatus, the group of movements based on the social media data.   
     
     
         5 . The method of  claim 1 , wherein modifying the workout comprises:
 detecting, by the fitness genome apparatus, a motion associated with the user;   determining, by the fitness genome apparatus, a training regime having difficulty increasing over time; and   decreasing the group repetition count.   
     
     
         6 . The method of  claim 1 , wherein modifying the workout comprises:
 detecting, by the fitness genome apparatus, a motion associated with the user;   determining, by the fitness genome apparatus, a training regime having difficulty decreasing over time; and   increasing the group repetition count.   
     
     
         7 . The method of  claim 1 , comprising:
 classifying, by the fitness genome apparatus, the workout based on a property function, a distance function, and an optimization function;   wherein the property function quantifies at least one of a duration of the workout, a energy expenditure of the workout, and an average power of the workout;   wherein the distance function quantifies a similarity of the workout and a second workout; and   wherein the optimization function comprises a mutation operator and the property functions.   
     
     
         8 . The method of  claim 7 , comprising modifying the workout using the optimization function. 
     
     
         9 . The method of  claim 1 , comprising:
 detecting, by the fitness genome apparatus, training deficiencies based on the performance data;   generating, by the fitness genome apparatus, suggestions to improve the performance data; and   displaying, by the fitness genome apparatus, the suggestions on a screen of the computing device.   
     
     
         10 . The method of  claim 1 , comprising:
 determining, by the fitness genome apparatus, muscles targeted by the movement;   identifying, by the fitness genome apparatus, a stretch exercise associated with the movement; and   displaying, by the fitness genome apparatus and on a screen of the computing device, a title of the stretch exercise, a description of the stretch exercise, and a figure performing the stretch exercise.   
     
     
         11 . A system to form a workout, the system comprising:
 a computing device communicatively coupled with a server, the computing device configured to:
 identify a movement associated with a user; 
 determine a movement repetition count associated with the movement; 
 identify a group of movements, wherein the group of movements includes a plurality of movements and a plurality of movement repetition counts corresponding to each of the plurality of movements; 
 determine a group repetition count for the group of movements; 
 form a workout based on the group of movements and the group repetition count; and 
 modify the workout based on performance data received from use of the workout. 
   
     
     
         12 . The system of  claim 10 , comprising the computing device configured to:
 receive, physical characteristic data associated with the user;   detect, a motion associated with the user; and   identify the movement based on at least one of the physical characteristic data and the motion associated with the user.   
     
     
         13 . The system of  claim 10 , comprising the computing device configured to:
 determine the group repetition count based on at least one of experience data associated with the user, a training regime selected by the user, and historical workout data associated with the user.   
     
     
         14 . The system of  claim 10 , comprising the computing device configured to:
 receive, from the server, social media data comprising a list of people the user follows on a social media group; and   identify the group of movements based on the social media data.   
     
     
         15 . The system of  claim 10 , comprising the computing device configured to:
 detect a motion associated with the user;   determine a training regime having difficulty increasing over time; and   decrease the group repetition count to modify the workout.   
     
     
         16 . The system of  claim 10 , comprising the computing device configured to:
 detect a motion associated with the user;   determine a training regime having difficulty decreasing over time; and   increase the group repetition count to modify the workout.   
     
     
         17 . The system of  claim 10 , comprising the computing device configured to:
 classify the workout based on a property function, a distance function, and an optimization function;   wherein the property function quantifies at least one of a duration of the workout, a calorie consumption of the workout, and an average power of the workout;   wherein the distance function quantifies a similarity of the workout and a second workout; and   wherein the optimization function comprises a mutation operator and the property functions.   
     
     
         18 . The system of  claim 17 , comprising the computing device configured to modify the workout based on the optimization function. 
     
     
         19 . The system of  claim 10 , comprising the computing device configured to:
 detect training deficiencies based on the performance data;   generate suggestions to improve the performance data; and   display the suggestions on a screen of the computing device.   
     
     
         20 . The system of  claim 10 , comprising the computing device configured to:
 determine muscles targeted by the movement;   identify a stretch exercise associated with the movement; and   display, on a screen of the computing device, a title of the stretch exercise, a description of the stretch exercise, and figure performing the stretch exercise.