Learned Model for Personalized Mental Fatigue Estimation
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Solution Overview
Problem
Existing mental and physical condition estimation systems struggle to accurately and subjectively indicate total mental fatigue, concentration levels, and multiple types of mental fatigue, relying on limited biological information and lacking personalized models.
Innovation Solution
An information processing apparatus and method that acquires biological information from users and employs a learned model to determine subjective indicators of total mental fatigue, concentration, and multiple types of mental fatigue, providing personalized and accurate feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing mental and physical condition estimation systems use limited biological information, then the system complexity is reduced, but the measurement precision of mental fatigue and concentration levels deteriorates
Solution Approach 1:
The system segments mental fatigue assessment into multiple dimensions: total mental fatigue level, concentration level, and multiple types of mental fatigue (physical fatigue, mental fatigue, emotional fatigue). Each dimension is estimated using specific biological information patterns learned by the model, allowing precise multi-faceted measurement without requiring a single complex system
Solution Approach 2:
The learned model transforms limited biological information into multiple psychological parameters through pattern recognition. By changing the parameter representation from raw biological data to derived psychological indicators (fatigue level, concentration level, fatigue types), the system achieves high measurement precision with limited input data
2Ease of operation
If existing systems lack personalized models, then the ease of operation is improved, but the reliability of mental fatigue estimation deteriorates
Solution Approach 1:
The system performs self-personalization through the learned model that automatically adapts to individual user patterns. The model learns from each user's biological information patterns and provides personalized fatigue estimates without requiring manual configuration or user input, maintaining ease of operation while improving reliability through individualized assessment
3Loss of information
If the system provides comprehensive subjective indicators of total mental fatigue and multiple types of mental fatigue, then the loss of information is reduced, but the device complexity increases
Solution Approach 1:
The learned model serves multiple functions simultaneously: it estimates total mental fatigue level, concentration level, and multiple types of mental fatigue (physical, mental, emotional fatigue) from the same biological information input. This multi-functionality reduces information loss by providing comprehensive assessment without requiring separate systems for each measurement type
Data Source
AI summary
An information processing apparatus includes circuitry to acquire information including biological information of a user, and cause a display to display at least one of a subjective indicator of total mental fatigue, a subjective indicator of concentration, or multiple types of mental fatigue determined based on the acquired information and a learned model. The learned model has learned relations between biological information acquired in advance and the subjective indicator of total mental fatigue, the subjective indicator of concentration, or each of the multiple types of mental fatigue.