Exercise Recommendation Variance Control for Personalized Workouts
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Solution Overview
Problem
Current workout planning systems fail to provide users with variety in their workouts while accounting for their goals and preferences, and they lack the ability to predict and adjust user capabilities based on previous exercises and workout frequency.
Innovation Solution
An exercise recommendation system that uses machine learning to select exercises, weights, and capabilities for users based on their profiles, exercise histories, available equipment, and goals, while also allowing users to specify their desired level of variance in workouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current workout planning systems are used, then workout plans can be generated, but users cannot specify their desired level of variety while accounting for their goals and preferences
Solution Approach 1:
The system introduces a variance parameter that allows users to specify their desired level of workout variety. The exercise selection model uses this parameter to adjust the randomness in exercise selection, enabling users to control the balance between variety and consistency in their workouts while still accounting for their goals and preferences.
2Measurement precision
If current systems determine workouts without capability prediction, then workout plans can be generated quickly, but they cannot predict user capabilities based on previous exercises and workout frequency
Solution Approach 1:
The system performs preliminary actions by continuously tracking and storing user exercise history, workout frequency, and performance data. This historical information is used to predict user capabilities before each workout session, enabling the system to adapt workout recommendations to the user's current fitness level without requiring complex real-time measurements.
Solution Approach 2:
The system implements feedback mechanisms by monitoring user performance data from previous workouts and using this information to update capability predictions. The exercise selection model incorporates this feedback to continuously improve its accuracy in predicting user capabilities, allowing for more precise workout recommendations over time.
3Measurement precision
If the system predicts user capability based on exercise history, then capability accuracy improves, but the time to generate workout plans increases
Solution Approach 1:
The system performs capability prediction in advance by pre-processing and storing user exercise history and performance data. This preliminary action allows the system to quickly retrieve and use historical information during workout plan generation, reducing real-time computation time while maintaining high prediction accuracy.
Data Source
AI summary
An exercise recommendation system determines workout plans for users. The exercise recommendation system receives a profile of a user and a level of variance selected by the user. The profile includes a history of exercises the user has performed, available gym equipment, and exercise goals. The exercise recommendation system inputs the profile to a machine learning model configured to rank a set of exercises for a user to perform. The exercise recommendation system modifies the ranking of exercises based on the level of variance selected by the user. Modification of the ranking is greater for a first level of variance than for a second level of variance less than the first level of variance. The exercise recommendation system generates a workout plan for display within a user interface to the user based on the modified ranking.