Adaptive Cognitive Training Program for Mild Cognitive Impairment
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Solution Overview
Problem
Existing methods for improving cognitive impairment symptoms in dementia patients are inadequate, and there is a need for effective training programs that can prevent or diagnose dementia at an early stage and reduce associated social costs.
Innovation Solution
A digital training program utilizing direct and indirect training algorithms, including visualization, fusion, semantic word fluency, working memory, and cognitive agility exercises, is implemented through a management server and user terminals, with adaptive rearrangement based on user performance to enhance cognitive function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional training methods are used for cognitive impairment patients, then the training program structure is simple and easy to implement, but the effectiveness in improving cognitive functions is insufficient
Solution Approach 1:
The training program is divided into multiple distinct training algorithms, each targeting specific cognitive functions (e.g., attention, memory, executive function). This segmentation allows the system to address different cognitive domains separately, improving overall effectiveness while maintaining manageable complexity through modular design
Solution Approach 2:
The training program dynamically adapts its structure and content based on patient performance and progress. The system adjusts training parameters, selects appropriate algorithms, and modifies difficulty levels in real-time, transforming a static program into a dynamic one that optimizes therapeutic effect while adapting to individual patient needs
2Reliability
If comprehensive training algorithms are implemented to cover all cognitive functions, then the training effectiveness is maximized, but the system complexity and implementation difficulty increase
Solution Approach 1:
The management server is designed as a universal platform that can execute multiple different training algorithms through a single integrated system. This multi-functional design allows comprehensive coverage of various cognitive functions without requiring separate systems for each training type, thereby managing complexity while maintaining comprehensiveness
Solution Approach 2:
The management server acts as an intermediary between the user terminals and the training algorithms. It handles the complexity of algorithm selection, parameter adjustment, and progress tracking, while presenting a simplified interface to users. This intermediary role isolates system complexity from end-users and facilitates comprehensive training delivery
3Adaptability or versatility
If fixed training programs are used for all patients, then the implementation is straightforward and consistent, but the adaptability to individual patient needs is poor
Solution Approach 1:
The system continuously collects feedback from patient performance during training sessions and uses this information to adapt the training program. The management server analyzes performance data and adjusts future training selections and parameters accordingly, creating a closed-loop system that improves adaptability while maintaining operational simplicity through automated feedback processing
Solution Approach 2:
The system performs preliminary assessment and analysis of patient needs before initiating the training program. By pre-evaluating cognitive function levels and identifying specific areas requiring improvement, the system can pre-select appropriate training algorithms and configure optimal parameters, thereby achieving high adaptability without complicating the actual training implementation
Data Source
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AI summary
Provided is a method of constructing a training program for improving symptoms of a mild cognitive impairment patient, the method including: arranging, on designated days, direct training algorithms regarding at least one of visualization, fusion, and semantic word fluency directly related to a cognitive function area of a person; controlling the arranged direct training algorithms to be output to a user terminal according to the designated days, and receiving a result value regarding the direct training algorithms from the user terminal; calculating an achievement level for each of the direct training algorithms, based on the result value, and determining one of the direct training algorithms based on the calculated achievement level; and rearranging the direct training algorithms on the designated days, together with the determined direct training algorithm, wherein the rearranging considers both the calculated achievement level and a training algorithm matched to the determined direct training algorithm.