Dynamic Exercise Goal Algorithm for Personalized Fitness Management
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Solution Overview
Problem
Existing tools for promoting exercise, such as pedometer-based daily step goals, fail to consider individual user factors like historical performance, motivational structure, and health data, leading to ineffective exercise management and weight control.
Innovation Solution
A method that identifies a raw target exercise level for a user and applies a goal-setting algorithm to adjust it based on user data, including historical performance, motivational analysis, and health data, to display tailored daily exercise goals, encouraging consistent activity throughout the day.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic medical recommendations or user selection are used to set daily exercise goals, then the goal setting process is simple, but the effectiveness of exercise management is reduced due to lack of individualization
Solution Approach 1:
The system dynamically adjusts exercise goals based on real-time analysis of user data including historical performance, motivational structure, and health data. The goal setting algorithm continuously adapts to changing user conditions, transforming static generic goals into dynamic personalized targets that evolve with the user's capabilities and circumstances.
Solution Approach 2:
The system changes multiple parameters simultaneously including goal magnitude, timing distribution, and adjustment frequency based on analyzed user characteristics. By modifying these parameters according to individual historical data and current state, the system generates optimized goals that balance simplicity with effectiveness.
2Reliability
If personalized goals based on user data analysis are implemented, then exercise management effectiveness is improved, but system complexity increases due to data collection and processing requirements
Solution Approach 1:
The goal setting algorithm serves multiple functions simultaneously: it analyzes historical performance data, evaluates motivational structure, processes health data, determines optimal goal magnitudes, and schedules goal adjustments. By consolidating these functions into a single multi-functional algorithm, the system reduces overall complexity while maintaining personalized effectiveness.
Solution Approach 2:
The system automatically collects, processes, and analyzes user data without requiring manual intervention. The goal setting algorithm self-adjusts based on incoming data, performing data processing and goal optimization autonomously. This self-service approach reduces operational complexity while enabling continuous personalization.
3Ease of operation
If daily exercise goals are set without considering historical performance and motivational structure, then the system is easier to operate, but user adherence and goal achievement are reduced
Solution Approach 1:
The system continuously monitors user progress against set goals and uses this feedback to adjust future goal recommendations. By analyzing whether users meet goals, when they meet them, and how they respond to different goal levels, the system refines its algorithm to produce more achievable targets that actually improve user adherence and achievement rates.
Data Source
AI summary
A method and apparatus for identifying a raw target exercise level for a user and applying a goal setting algorithm to adjust the raw target exercise level for the user, the adjustment made in accordance with analysis of the user's data, and displaying an adjusted target exercise level to the user, in order to achieve the raw target exercise level for the user.