User effort measurement during workout session

A fitness device, comprising:

• at least one processor;
• and
• a memory storing instructions thereon that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• receiving, by a first machine learning model, one or more features related to a workout session;
• generating, by the first machine learning model based on the one or more features, a first output comprising an estimated classification of user effort for the workout session in a particular category of a plurality of known categories;
• receiving, by a second machine learning model, the one or more features and the estimated classification output by the first machine learning model;
• and
• generating, by the second machine learning model and based on the one or more features and the estimated classification, a second output comprising an estimated score of the user effort for the workout session.

A non-transitory, computer-readable storage medium having instructions stored thereon, that when executed by at least one processor, cause the at least one processor to perform operations comprising:

• receiving, by a first machine learning model, one or more features related to a workout session;
• generating, by the first machine learning model based on the one or more features, a first output comprising an estimated classification of user effort for the workout session in a particular category of a plurality of known categories;
• receiving, by a second machine learning model, the one or more features and the estimated classification output by the first machine learning model;
• and
• generating, by the second machine learning model and based on the one or more features and the estimated classification, a second output comprising an estimated score of the user effort for the workout session.