Exercise Recommendation System Using Moving Average Capability
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Solution Overview
Problem
Current workout systems fail to provide users with personalized exercise plans that account for their preferences, including the desire for variety and progressive increases in capability, as they do not effectively utilize user history and equipment availability to recommend exercises and weights.
Innovation Solution
An exercise recommendation system that uses machine learning to rank exercises based on user history, available equipment, and goals, while adjusting for variance preferences and calculating current capabilities by analyzing exercise performance statistics and recency, to generate tailored workout plans and recommend target weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current systems provide workout plans based on user goals and preferences, then user engagement is improved, but the ability to predict user capability and provide progressive challenges is insufficient
Solution Approach 1:
The system performs preliminary actions by predicting user capability before workout sessions. It analyzes historical workout data, exercise performance statistics, and recency information to determine current capability levels in advance, enabling the system to prepare appropriate progressive challenges before the user actually performs the exercises.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user workout performance and using this information to refine capability predictions. Historical workout data and exercise performance statistics provide feedback loops that improve the accuracy of capability prediction models over time, allowing for more precise personalization.
2Measurement precision
If the system analyzes detailed user history and performance statistics to improve capability prediction, then measurement precision improves, but system complexity increases
Solution Approach 1:
The system extracts relevant information from comprehensive user history data by focusing on specific key elements: exercise performance statistics, recency of workouts, and historical capability measurements. This extraction approach allows the system to achieve accurate capability prediction without processing all available data, thereby reducing computational complexity while maintaining precision.
Solution Approach 2:
The system segments the capability prediction process into distinct components: analyzing historical workout data, evaluating exercise performance statistics, assessing recency information, and synthesizing these into current capability determinations. This segmentation allows each component to be processed independently and efficiently, reducing overall system complexity.
3Adaptability or versatility
If the system provides variety in workout exercises to meet user preferences, then user engagement improves, but consistency with user goals and capability progression may be compromised
Solution Approach 1:
The system applies local quality by tailoring workout specifics to match user goals while maintaining variety. It analyzes individual exercise performance statistics and capability levels to determine which exercises should be varied and which should remain consistent, ensuring that variety is applied selectively where it enhances engagement without compromising goal alignment.
Solution Approach 2:
The system implements dynamics by making workout plans adaptive and flexible. It continuously adjusts exercise selections, weights, and repetitions based on real-time capability predictions and user performance, allowing the workout to dynamically balance variety with goal consistency. The system can shift between prioritizing variety and prioritizing consistency based on current user capabilities and preferences.
Data Source
AI summary
An exercise recommendation system determines a current capability of a user. The exercise recommendation system accesses an exercise history for a user. The exercise history comprises an exercise performed by the user and a capability of the user each time the user performed the exercise. The exercise recommendation system partitions the exercise history into a plurality of time periods, and, for each time period, computes an aggregate capability of the user for the exercise during the time period. The exercise recommendation system calculates a moving average capability of the user for the exercise based on the aggregate capabilities and determines a current capability of the user for the exercise based on the moving average capability. The current capability of the user may be discounted at least in part based on how recently the user performed the exercise.