Mobile Cognitive Assessment Using Adaptive Touchscreen Tasks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for evaluating cognitive abilities are imprecise, cumbersome, and one-dimensional, often requiring clinical supervision and failing to account for individual variability and natural learning progression, limiting the detection of cognitive impairment in neurodegenerative diseases.
Innovation Solution
A method using a mobile device to administer adaptive cognitive tests, adjusting task difficulty based on user performance characteristics and comparing them to previous tasks, incorporating spatial and temporal properties of user inputs, and utilizing machine learning to improve evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical supervised tests are administered in healthcare facilities, then evaluation standardization is improved, but observation bias and subjectivity increase
Solution Approach 1:
The system enables users to autonomously perform cognitive assessments at home using their mobile devices without requiring clinical supervision. The automated task administration and performance analysis eliminate observer bias while maintaining evaluation consistency through standardized algorithms that automatically process user interactions and generate cognitive ability scores.
2Measurement precision
If in-clinic tests are administered under medical supervision, then test administration control is improved, but time consumption and cumulative burden increase
Solution Approach 1:
The system implements periodic cognitive assessments that users can complete at home over time, transforming a single time-consuming clinic visit into multiple shorter assessment sessions distributed across different time points. This periodic home-based testing reduces cumulative time burden while accumulating sufficient data for precise cognitive evaluation through longitudinal tracking.
3Ease of operation
If one-size-fits-all fixed difficulty tests are used, then test administration simplicity is improved, but sensitivity to individual variability and learning progression decreases
Solution Approach 1:
The system dynamically adjusts task difficulty levels based on each user's performance characteristics and learning progression. The adaptive engine monitors user responses in real-time and automatically modifies subsequent task parameters, transforming static fixed-difficulty tests into dynamic assessments that optimize sensitivity to cognitive changes while maintaining ease of administration through automated adaptation.
Solution Approach 2:
The system changes multiple task parameters simultaneously including difficulty level, task type, and presentation format based on user performance. By dynamically modifying these parameters, the system maintains optimal challenge levels that maximize detection sensitivity while accounting for individual baseline abilities and natural learning curves.
4Device complexity
If single-aspect cognitive tests are administered, then test complexity is reduced, but comprehensive cognitive evaluation capability decreases
Solution Approach 1:
The mobile device-based system serves multiple cognitive assessment functions through a unified platform, evaluating diverse cognitive domains including memory, attention, executive function, and processing speed. By consolidating multiple assessment capabilities into a single adaptive system, the approach maintains manageable complexity while achieving comprehensive cognitive evaluation that exceeds single-aspect tests.
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
In an aspect, the disclosure relates to a method for determining a user's cognitive abilities. The method comprises providing, by a mobile device, a sequence of first tasks, to be performed by a user of the mobile device, based on displaying one or more first objects at respective one or more first positions on a touchscreen display of the mobile device, and detecting, while the user is performing the sequence of first tasks, one or more user inputs related to the one or more first objects on the touchscreen display. The method further comprises determining, for each respective first task of the sequence of first tasks, a user performance characteristic based on spatial and temporal properties of the one or more user inputs on the touchscreen display, and evaluating, for each respective first task of the sequence of first tasks, a cognitive ability of the user based on a comparison of the determined performance characteristic of the respective first task with the determined performance characteristic of a preceding first task or the determined performance characteristics of a plurality of preceding first tasks of the sequence of the first tasks.