Information Processing Apparatus for Early Dementia Detection
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Solution Overview
Problem
Existing methods for detecting dementia and other mental illnesses at an early stage are ineffective as they rely on specific symptoms that may not appear until the condition has progressed, making it difficult to sense signs of these illnesses before they become severe.
Innovation Solution
An information processing apparatus and method that senses changes in a reactant's state based on a time-series record of reactions to predetermined input patterns that occur frequently in the reactant's environment, such as greetings or requests, allowing for accurate detection of mental illness signs without requiring advanced symptom progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biological data is compared with dementia patients to determine dementia risk, then determination accuracy is improved, but early stage detection capability deteriorates because symptoms must progress to show strong characteristics
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing reaction data before dementia symptoms become apparent. It establishes baseline reaction patterns during normal cognitive function and detects deviations that indicate early-stage dementia, enabling intervention before strong symptom characteristics develop.
Solution Approach 2:
Instead of waiting for complete symptom manifestation, the system detects partial signs of dementia through subtle changes in reaction patterns. It analyzes multiple reaction dimensions (response time, accuracy, consistency) to detect early indicators that individually may be insufficient but collectively provide early detection capability.
2Measurement precision
If specific dementia symptoms are monitored, then determination accuracy is improved, but applicability to early stage cases deteriorates as symptoms may not appear until progression
Solution Approach 1:
The system segments dementia detection into multiple independent reaction dimensions including response time, accuracy, consistency, and pattern recognition. By analyzing each dimension separately and then integrating results, it can detect early-stage changes in individual dimensions that collectively indicate dementia before classic symptoms manifest.
Solution Approach 2:
The reaction pattern analysis system serves multiple functions: it monitors normal cognitive function, detects early dementia signs, tracks disease progression, and evaluates treatment effectiveness. This multi-functional approach enables early detection while maintaining accuracy across different disease stages.
3Loss of time
If continuous monitoring of reactions is performed, then early detection capability is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system extracts only the essential reaction features needed for dementia detection from continuous monitoring data. It identifies and focuses on key indicators such as response time variations, accuracy changes, and pattern inconsistencies, filtering out redundant information to simplify processing while maintaining early detection capability.
Solution Approach 2:
The system performs self-service by automatically analyzing reaction patterns and detecting dementia signs without requiring complex external processing. It maintains baseline profiles and autonomously compares new reactions against these baselines, reducing the need for sophisticated external analysis systems while enabling continuous monitoring.
Data Source
AI summary
To sense a sign of a predetermined state at an early stage. Provided is an information processing apparatus including a state sensing unit that senses a change in state of a reactant on the basis of a time-series record of reactions made by the reactant to at least one predetermined input pattern executed by an input body, in which the predetermined input pattern is an event that repeatedly occurs in an environment in which the reactant lives.