Adaptive Workout Recommendation System for Fitness Training
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Solution Overview
Problem
Current fitness training programs lack adaptability and flexibility, often relying on predetermined templates that do not account for individual user progress or goals, leading to ineffective workout recommendations.
Innovation Solution
A method and apparatus that dynamically determine a user's present fitness level, set training goals, and adjust workout recommendations based on historical training data to provide personalized and adaptive training plans, taking into account the user's activity class, training history, and specific fitness objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predetermined training program templates are used, then the program structure is simple and easy to implement, but the program lacks adaptability and flexibility to individual user needs
Solution Approach 1:
The training program transitions from static predetermined templates to a dynamic system that automatically adjusts workout recommendations based on real-time detection of user's present fitness level, training history, and progress toward goals. The system dynamically modifies training load targets and micro-cycle phases to adapt to individual user needs while maintaining program structure.
Solution Approach 2:
The system changes key training parameters (training load targets, micro-cycle phases, workout intensity) based on detected user characteristics and progress. By adjusting these parameters automatically according to measured fitness levels and historical data, the program achieves adaptability without requiring complex manual customization interfaces.
2Reliability
If personalized training recommendations are provided based on multiple user parameters, then training effectiveness is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system implements continuous feedback loops where workout detection results and training history data are fed back into the recommendation engine. This feedback mechanism allows the system to automatically refine workout recommendations based on actual user performance and progress, improving reliability through iterative optimization rather than complex upfront modeling.
Solution Approach 2:
The training system serves itself by automatically detecting fitness levels, analyzing training history, and generating optimized workout recommendations without external intervention. This self-service capability reduces the need for complex user input interfaces and manual program customization, simplifying the system while maintaining personalization.
Data Source
AI summary
The application presents providing next workout recommendation(s) for a user. The current training load for the next workout recommendation is determined based on a present activity class, a phase of the micro-cycle of successive training days and the sum of the training load values for a number of past trainings. The present activity class of the user corresponds to a present fitness level of the user. A total target training load is determined based on the present activity class and the training goal, which relates to maintaining, increasing or increasing fast the fitness level of the user. The phase of the micro-cycle of successive training days for the next workout recommendation is determined by the realized training load value(s) for daily training session(s) of at least one previous day from training history data, as a percentage of the sum of the training load values for a number of past trainings.