Personalized Exercise Model Generation via User Classification
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Solution Overview
Problem
Current technologies do not adequately address the increasing demand for personalized exercise services, particularly in an aging society, by failing to provide tailored exercise models based on user characteristics such as sex, age, and body type, leading to suboptimal exercise experiences.
Innovation Solution
A personalized exercise service apparatus and method that includes a user class identifier, exercise data acquirer, and model generator to classify users based on sex, age, and body type, using image recognition and visual sensors to generate personalized exercise models by comparing user data with standard models, and controlling exercise equipment for optimal alignment and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a standardized exercise program is provided to all users, then the service coverage and simplicity are improved, but the personalization and effectiveness for individual users deteriorates
Solution Approach 1:
The patent segments users into different classes based on sex, age, and body type characteristics. By dividing the user population into distinct groups (young males, elderly females, etc.), the system can provide standardized exercise programs tailored to each segment's specific needs, thereby achieving personalization without excessive complexity in the overall service structure.
Solution Approach 2:
The system changes key parameters such as exercise intensity, duration, and type based on user class characteristics. For example, it adjusts exercise programs according to age-related physiological changes, body type variations, and sex-specific requirements, allowing the same basic framework to deliver personalized content through parameter modification rather than complete program redesign.
2Reliability
If personalized exercise models are developed for each user, then the exercise effectiveness is improved, but the system complexity and data processing requirements worsen
Solution Approach 1:
The patent performs preliminary classification of users into distinct classes before providing exercise programs. By pre-categorizing users based on sex, age, and body type, the system establishes a standardized framework that simplifies subsequent personalization processes, avoiding the need for complex real-time analysis of each individual user.
Solution Approach 2:
The system creates universal exercise models for each user class that can serve multiple users with similar characteristics. Instead of developing completely individualized programs for each user, the system uses multi-functional standardized models that can be applied across groups, reducing overall system complexity while maintaining effectiveness through class-based personalization.
3Measurement precision
If detailed user characteristics are collected and analyzed, then the accuracy of personalized exercise models is improved, but the time and computational resources required worsen
Solution Approach 1:
The patent segments user characteristics into three key categories: sex, age, and body type. By focusing on these specific segments rather than collecting and analyzing all possible user data, the system achieves sufficient classification accuracy with minimal data processing time, balancing precision and efficiency.
Solution Approach 2:
The system changes the approach from analyzing continuous detailed user data to using discrete parameter categories (sex: male/female, age: young/elderly, body type: specific classifications). This parameter transformation enables accurate user classification while significantly reducing computational complexity and processing time.
Data Source
AI summary
Disclosed are a personalized exercise service providing method and an apparatus thereof. A personalized exercise service providing apparatus according to an example includes a user class identifier configured to identify user class of a user; an exercise data acquirer configured to acquire exercise data of the user; and a model generator configured to generate a personalized exercise model of the user based on a standard exercise model corresponding to the determined user class and the acquired exercise data of the user to provide the most effective exercise model to each user through user recognition.