Abstract: A classification model is generated based on historical exercise information. User exercise information is classified into an exercise category using the classification model. Recommendations based on the exercise category is identified. A customized exercise recommendation is determined from the identified recommendations based on a comparison of the user exercise information and expected progress data. This customized recommendation is provided to a user device for consumption.

Abstract: Embodiments herein provide systems, methods, and computer-readable medium for providing customized exercise-related recommendations. Utilizing machine learning algorithms, a classification model may be trained with fitness-related information (e.g., exercise information, user profile information, and/or vital sign information) of a group of users. The classification model may be configured to output a classification for input data (e.g., fitness-related information of a particular user). A recommendation corresponding to a classification may be identified and provided to a particular user. User compliance with provided recommendations and subsequent user progress may be tracked to determine when recommendations were effective at bringing about a desired result (e.g., progressing the user toward a goal). Additionally, the system may determine when classifications have been inaccurately determined and/or when expected progress data has not provided a realistic path by which a user may progress toward a goal.

Abstract: Embodiments herein provide systems, methods, and computer-readable medium for providing customized exercise-related recommendations. Utilizing machine learning algorithms, a classification model may be trained with fitness-related information (e.g., exercise information, user profile information, and/or vital sign information) of a group of users. The classification model may be configured to output a classification for input data (e.g., fitness-related information of a particular user). A recommendation corresponding to a classification may be identified and provided to a particular user. User compliance with provided recommendations and subsequent user progress may be tracked to determine when recommendations were effective at bringing about a desired result (e.g., progressing the user toward a goal). Additionally, the system may determine when classifications have been inaccurately determined and/or when expected progress data has not provided a realistic path by which a user may progress toward a goal.