Adaptive Mood Index Calculation Using Personal Trait Selection
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Solution Overview
Problem
Existing methods for measuring mood states using brain activation data from working memory tasks are inaccurate due to variations in individual WM capacity and cognitive task performance, which affect the reliability of mood indices.
Innovation Solution
A system that stores multiple calculation formulas for mood indices and selects the appropriate one based on personal trait information, including WM capacity and task performance, to calculate more accurate mood indices from brain activation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single calculation formula is used for mood indices regardless of individual differences, then the measurement process is simple, but the accuracy of mood state measurement deteriorates due to variations in WM capacity and task performance
Solution Approach 1:
The system dynamically selects different calculation formulas based on the subject's WM capacity and task performance levels. Instead of using a fixed single formula, the system adapts the calculation method according to individual characteristics and performance data, thereby improving measurement accuracy while managing complexity through automated selection
Solution Approach 2:
The system changes the parameter of calculation formula selection based on measured parameters (WM capacity and task performance). By adjusting which calculation formula is applied according to these measured parameters, the system optimizes mood state measurement accuracy for different individuals and performance levels
2Measurement precision
If multiple calculation formulas are used to account for individual differences, then the accuracy of mood state measurement improves, but the complexity of the measurement system increases
Solution Approach 1:
The system performs self-service by automatically selecting the appropriate calculation formula based on the subject's WM capacity and task performance data. This automated selection process eliminates the need for manual intervention to choose formulas, reducing operational complexity while maintaining high measurement accuracy through adaptive formula selection
3Reliability
If WM capacity and task performance are considered in the measurement, then the reliability of mood indices improves, but the measurement process becomes more complex
Solution Approach 1:
The system performs preliminary action by measuring and evaluating WM capacity and task performance before calculating mood indices. These preliminary measurements are used to select the appropriate calculation formula, ensuring that the mood state measurement is based on reliable individual characteristics and performance data, thereby improving reliability while managing processing complexity through structured pre-processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables higher-accurate mood state measurement by accounting for individual differences in WM capacity, improving the correlation between brain activation data and mood scores, especially when selecting calculation formulas based on task performance.
Implementation Method 1
measuring brain activation data obtained by a near-infrared spectroscopy
Data Source
AI summary
Disclosed is an system for measuring mood states, which calculate and views high-accurate mood indices from brain activation signals obtained by a non-invasive biospectrometric technology, taking account into differences among individuals, for example cognitive capacity using methods supporting the measurement of mood states of individuals. The system of the present invention stores a plurality of different mood index calculation formulae, selects one of the plurality of mood index calculation formulae based on a personal trait, for example the working memory task performance or working memory capacity of a subject, and substitutes the brain activation data of the subject for the selected calculation formula to calculate and view mood indices.