Dementia Detection via Activity and Device Data Fusion
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Solution Overview
Problem
Existing dementia detection systems are insufficient in accurately identifying signs of dementia in individuals, as they fail to effectively detect behavioral and psychological symptoms.
Innovation Solution
A dementia symptom detection system that includes activity amount detectors, device operation information obtainment units, and a processing unit to determine dementia levels based on reference and personal values derived from historical activity and device operation data, allowing for the appropriate detection and presentation of dementia signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dementia detection system uses only unusual behavior detection methods, then the system complexity is low, but the measurement precision of dementia signs is insufficient
Solution Approach 1:
The patent combines multiple data sources including activity amount data from detectors, device operation information from various appliances, and historical data into a unified analysis system. This integration of heterogeneous data streams enables comprehensive dementia detection while maintaining manageable system complexity through standardized processing protocols.
Solution Approach 2:
The system employs a multi-functional approach by using a single integrated platform that processes diverse data types (activity detection, device operations, historical records) through common analytical methods. The information processor serves multiple functions including data collection, historical comparison, pattern recognition, and dementia level determination, reducing overall system complexity.
2Measurement precision
If the system collects and stores historical activity and device operation data, then the measurement precision improves, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing activity amount data and device operation information in historical databases before actual dementia assessment is needed. This pre-processing enables rapid comparison and analysis during clinical evaluation, reducing real-time processing requirements and improving detection speed.
3Measurement precision
If the system uses multiple indices for behavioral and psychological symptoms, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system employs parameter changes by transforming diverse clinical observations and data inputs into standardized quantitative indices that can be systematically processed. Multiple behavioral and psychological symptom indices are calculated from raw data, enabling precise dementia level determination while maintaining processing efficiency through consistent mathematical transformations.
Data Source
AI summary
A dementia symptom detection system includes an information processor which determines the level of dementia of a user based on (i) a reference value which is for an index corresponding to behavioral and psychological symptoms of dementia and which is determined based on the history of the activity amount stored in storage and the history of device operation information stored in the storage, and (ii) a personal value of the user which is for the index and which is determined based on the detected activity amount of the user and the obtained device operation information of the user.