Methods and systems for generating physical activity sets for a human subject

1. A system for generating physical activity sets for a human subject, the system comprising:

a computing device configured to:

detect a signal from at least a wearable fitness device;

determine an activity profile, wherein determining the activity profile further comprises;

generating an activity machine-learning model, wherein the activity machine-learning model is trained with training data that includes a plurality of entries and each entry correlates human subject signals to a plurality of human subject activity metrics; and

determining, using the activity machine-learning model and the signal, the activity profile;

generate, using the activity profile, at least a physical activity set wherein generating the at least a physical activity set further comprises:

classifying the activity profile to corresponding activity profile data from a plurality of human subjects, wherein the corresponding activity profile data correlates activity profile data to at least a physical activity;

training a fitness machine-learning model using the corresponding activity profile data; and

generating the at least a physical activity set as a function of the activity profile data and the fitness machine-learning model; and

present the activity profile and the at least a physical activity set using a graphical user interface.

2. The system of claim 1 , wherein at least a wearable fitness device includes a sensor.

3. The system of claim 2 , wherein the computing device is configured to classify, using a classification machine-learning process, the signal to at least an element of human subject data.

4. The system of claim 1 , wherein the computing device is configured to provide a real-time fitness update during a human subject activity using reactive computing, wherein the real-time fitness update is provided as a change in an element of human subject data of a human subject during collection of the signal.

5. The system of claim 4 , wherein providing the real-time fitness update further comprises storing the real-time fitness update in a database.

6. The system of claim 1 , wherein generating the at least a physical activity set for the human subject further comprises:

identifying at least a fitness activity contained within an activity profile; and

generating a plurality of physical activities as a function of the fitness machine-leaning model and the at least a fitness activity;

ranking the plurality of physical activities, wherein ranking further comprises using a ranking machine-learning process to rank the plurality of physical activities as a function of how well the physical activity set matches to the at least a fitness activity; and

selecting the at least a physical activity set as a function of the ranking.

7. The system of claim 1 , wherein generating the at least a physical activity set further comprises:

generating a classifier as a function of a classification machine-learning process; and

filtering a plurality of physical activities as a function of the classifier.

8. The system of claim 1 further comprising:

generating, using the at least a physical activity set, a physical activity instruction set, wherein the physical activity instruction set comprises instructions to implementing the at least a physical activity set; and

displaying a representation of the physical activity instruction set via a graphical user interface.

9. The system of claim 1 , wherein the graphical user interface is configured to accept human subject questionnaire data corresponding to at least a human subject activity and at least a physical activity set.

10. The system of claim 1 , wherein the computing device is further configured to provide notifications during a human subject activity as a function of the human subject specific pattern.

11. A method for generating physical activity sets for a human subject, the method comprising:

a computing device configured for:

detecting a signal from the at least a wearable fitness device;

determining an activity profile, wherein determining the activity profile further comprises;

generating an activity machine-learning model, wherein the activity machine-learning model is trained with training data that includes a plurality of entries and each entry correlates human subject signal to a plurality of human subject activity metrics;

determining, using the activity machine-learning model and the signal, the activity profile;

generating, using the activity profile, at least a physical activity set wherein generating at least a physical activity set further comprises:

classifying the activity profile to corresponding activity profile data from a plurality of human subjects, wherein the corresponding activity profile data correlates activity profile data to at least a physical activity set;

training a fitness machine-learning model using the corresponding activity profile data; and

generating the at least a physical activity set as a function of the activity profile data and the fitness machine-learning model; and

presenting the activity profile and the at least a physical activity set using a graphical user interface.

12. The method of claim 11 , wherein at least a wearable fitness device includes a sensor.

13. The method of claim 12 , wherein the computing device is configured to classify, using a classification machine-learning process, the signal to at least an element of human subject data.

14. The method of claim 11 , wherein the computing device is configured to provide a real-time fitness update during a human subject activity using reactive computing, wherein the real-time fitness update is provided as a change in an element of human subject data of a human subject during collection of the signal.

15. The method of claim 14 , wherein providing the real-time fitness update further comprises storing the real-time fitness update in a database.

16. The method of claim 11 , wherein the graphical user interface is configured to accept human subject questionnaire data corresponding to at least a human subject activity and at least a physical activity set.

17. The method of claim 11 , wherein the computing device is further configured to provide notifications during a human subject activity as a function the human subject specific pattern.

18. The method of claim 11 , wherein selecting the at least a physical activity set for the human subject further comprises:

identifying at least a fitness activity contained within an activity profile; and

querying for the physical activity set associated with the fitness activity, wherein querying further comprises using the fitness machine-learning model to generate a plurality of physical activity sets;

ranking the plurality of physical activity set, wherein ranking further comprises using a ranking machine-learning process to rank at least a physical activity set as a function of how well the physical activity set matches to the at least a fitness activity; and

selecting the at least a physical activity set as a function of the ranking.

19. The method of claim 11 further comprising:

generating, using a selected physical activity set, a physical activity instruction set, wherein the physical activity instruction set comprises instructions to implementing the selected physical activity set; and

displaying a representation of the physical activity instruction set via a graphical user interface.

20. The method of claim 11 , wherein selecting the at least a physical activity set further comprises using the classification machine-learning process to generate a classifier, wherein the classifier is used by the computing device to filter physical activity set.