Fatigue Estimation Device Using Attribute-Based Normalization
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Solution Overview
Problem
Existing fatigue estimation systems fail to account for individual differences in fatigue levels, relying solely on biological data without considering subject-specific attributes.
Innovation Solution
A fatigue estimation device and method that performs a normalization process based on subject attributes, followed by fatigue level estimation using biological data, incorporating attribute information such as muscle mass, age, and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fatigue estimation is performed using only biological data without normalization, then the estimation process is simple, but the estimation accuracy deteriorates due to individual differences among test subjects
Solution Approach 1:
The patent applies preliminary action by performing normalization processing before fatigue estimation. The system pre-processes biological data by normalizing it based on individual attributes (age, gender, fitness level) stored in advance, thereby eliminating individual differences before the actual fatigue estimation occurs. This preliminary normalization step ensures that subsequent estimation is accurate without requiring complex individualized models during the estimation phase itself.
Solution Approach 2:
The patent changes parameters by transforming raw biological data into normalized data using subject-specific attributes. The system modifies the biological data parameters (such as heart rate, blood pressure) by applying normalization factors derived from individual characteristics, thereby converting absolute values into relative values that can be accurately compared across different individuals for fatigue estimation.
2Measurement precision
If normalization processing based on individual attributes is performed, then fatigue estimation accuracy is improved, but the data processing time increases
Solution Approach 1:
The system performs preliminary action by pre-storing individual attributes (age, gender, fitness level) and normalization parameters in a database before actual fatigue estimation. This allows the normalization process to quickly retrieve pre-computed factors rather than calculating them in real-time, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent applies copying by using pre-established normalization models and reference data for different individual types. Instead of performing complex calculations for each subject, the system copies and applies pre-determined normalization parameters corresponding to the subject's attribute category, thereby accelerating processing while preserving estimation accuracy.
3Adaptability or versatility
If only biological data is used for fatigue estimation, then the system is easy to operate, but it cannot account for individual differences among test subjects
Solution Approach 1:
The patent applies universality by creating a multi-functional system that handles both simple biological data collection and complex individualized normalization. The system universally processes different types of subjects (athletes, non-athletes, different ages, genders) through a single unified normalization framework, making it adaptable to various individuals while maintaining ease of operation through automated attribute-based processing.
Solution Approach 2:
The system implements self-service by automatically retrieving individual attributes and applying appropriate normalization without requiring manual intervention. The system autonomously identifies the subject's characteristics and applies the correct normalization parameters, thereby providing individualized adaptation while keeping the operation simple for the user.
Data Source
AI summary
In a fatigue estimation device mainly includes a normalization process means 14X, and a fatigue estimation means 16X. The normalization process means 14X performs a normalization process based on an attribute of a test subject with respect to biological data of the test subject. The fatigue estimation means 16X estimates a fatigue level of the test subject based on the biological data after the normalization process.