Personalized Fitness Guidance System Using Machine Learning
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Solution Overview
Problem
Existing connected fitness platforms lack personalized guidance in recommending exercise classes and activities based on individual user preferences, equipment availability, fitness levels, and specific goals, leading to a non-tailored user experience.
Innovation Solution
A guidance system that utilizes machine learning and artificial intelligence to generate personalized class sequences and recommendations for users based on their fitness goals, preferences, equipment availability, and historical activity data, continuously updating and modifying the recommendations in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a connected fitness platform provides general exercise class recommendations without personalization, then the system is simple and easy to operate, but the user experience lacks tailoring to individual preferences, fitness levels, and goals
Solution Approach 1:
The recommendation system is divided into multiple independent modules: a data collection module that gathers user preferences and fitness data, a machine learning model module that processes the data, and a recommendation generation module that outputs personalized class sequences. This segmentation allows the system to achieve personalization while maintaining manageable complexity through modular design.
Solution Approach 2:
A machine learning model serves as an intermediary between raw user data and recommendation outputs. The model processes diverse user inputs (preferences, fitness level, goals) and transforms them into personalized exercise class recommendations, bridging the gap between simple data collection and complex personalization requirements.
2Duration of action of moving object
If the platform offers static exercise recommendations, then the system is simple to implement, but it cannot adapt to real-time user performance and feedback
Solution Approach 1:
The system implements continuous feedback loops where user performance data and feedback during exercise classes are automatically collected, processed by machine learning models, and used to dynamically adjust subsequent class recommendations. This enables real-time adaptability while the automation handles the complex task of processing feedback and generating updated recommendations.
Solution Approach 2:
The recommendation system transitions from static pre-defined sequences to dynamic sequences that automatically adjust based on real-time user performance. The machine learning models continuously update recommendations as users progress through their fitness journey, making the system adaptive rather than fixed.
3Productivity
If personalized recommendations are generated manually, then the recommendations can be tailored to user needs, but the process is time-consuming and not scalable
Solution Approach 1:
The manual process of creating personalized recommendations is replaced with automated machine learning systems. The ML models process user data, preferences, and performance metrics automatically, generating personalized class sequences without human intervention. This substitution dramatically increases productivity while the comprehensive data processing ensures no user information is lost.
Solution Approach 2:
The system utilizes multiple user parameters simultaneously (fitness level, preferences, goals, performance data) to generate recommendations. By changing from single-parameter to multi-parameter analysis through machine learning, the system achieves both high productivity and comprehensive utilization of user information.
Data Source
AI summary
The systems and methods described herein receive a fitness goal or target (e.g., a user wants to be stronger, or run faster, or bike longer within a certain heart rate zone) as seed input into a guidance system, which generates a personalized plan of recommended classes/activities for the user based on their goal/target. At different points along the plan, the guidance system may modify or update its recommendations with different or enhanced classes/activities, in order to keep the user on their target or goal.