Phased Cognitive Training on User Terminals for Mild Impairment
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Solution Overview
Problem
The increasing prevalence of dementia and cognitive impairment in aging societies poses a significant challenge, with existing training methods being inadequate for effectively alleviating symptoms, particularly in patients with mild cognitive impairment.
Innovation Solution
A digital dementia therapeutic system utilizing a user terminal that implements a structured training program comprising multiple domain-specific algorithms, including attention, associative, and integrative skills training, with phased progression and assessment, to improve cognitive function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing training methods are used for cognitive impairment, then training can be provided to patients, but the training is inadequate for effectively alleviating symptoms particularly in patients with mild cognitive impairment
Solution Approach 1:
The training program is segmented into multiple phases (first phase and second phase) with different training algorithms for different cognitive domains (first domain, second domain, third domain). Each phase contains specific training algorithms that can be independently selected and executed, allowing the system to adapt to different impairment stages by activating appropriate phases and domains.
Solution Approach 2:
The system dynamically adjusts the training program based on user completion status. When all training algorithms in the first phase are completed, the system automatically progresses to the second phase. The visual indication also dynamically changes based on completion status, providing real-time feedback and adapting the training intensity and content.
2Reliability
If a comprehensive multi-phase training program is implemented, then cognitive function can be effectively improved, but the system complexity increases
Solution Approach 1:
The system implements continuous feedback mechanisms by detecting when training algorithms are completed and automatically adjusting the program progression. The visual indication provides clear feedback to users about their completion status, and the system uses this information to determine whether to progress to the next phase, creating a self-regulating training system.
Solution Approach 2:
The training system is designed to be self-managing through automatic phase progression. When all training algorithms in the current phase are completed, the system automatically transitions to the next phase without requiring manual intervention. This self-service mechanism reduces operational complexity while maintaining comprehensive training coverage.
3Reliability
If multiple domain training algorithms are provided in phased structure, then targeted cognitive training can be achieved, but the operation complexity for users increases
Solution Approach 1:
The system automatically manages the complex multi-phase training structure without requiring user intervention. Users simply need to complete the presented training algorithms, and the system handles phase transitions, algorithm selection, and progression logic automatically, making the complex system easy to operate.
Solution Approach 2:
The visual indication provides clear, intuitive feedback to users about their progress and completion status. This simple visual feedback mechanism guides users through the complex training program, reducing operational complexity by making the system state transparent and easy to understand.
Data Source
AI summary
A method of alleviating cognitive impairment includes performing control such that a first domain training algorithm, a second domain training algorithm, and a third domain training algorithm of a first phase are output on the user terminal, determining whether the first domain training algorithm, the second domain training algorithm, and the third domain training algorithm of the first phase are completed, and determining whether the first phase is normally terminated, performing control such that a first domain training algorithm, a second domain training algorithm, and a third domain training algorithm of a second phase are output on the user terminal, determining whether the first domain training algorithm, the second domain training algorithm, and the third domain training algorithm of the second phase are completed, and determining whether the second phase is normally terminated, and performing control such that a first visual indication is output on the user terminal.