Bimanual Finger Movement Analysis for Cognitive Impairment Screening
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Solution Overview
Problem
Existing methods for evaluating cognitive decline in dementia fail to effectively calculate differences in movement functions between both hands, limiting the ability to accurately assess cognitive impairment.
Innovation Solution
A brain dysfunction evaluation system that includes a storage unit, analysis unit, and display unit to analyze time-series data from both hands' finger movements, generating movement waveforms and difference-between-hands feature quantities to evaluate cognitive decline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cognitive function tests such as Hasegawa's dementia scale or MMSE are used, then diagnostic accuracy is improved, but test time and operational complexity increase
Solution Approach 1:
The patent replaces complex mechanical/cognitive testing procedures with an automated sensor-based measurement system. The sensor device objectively measures hand movement parameters (position, velocity, acceleration) without requiring doctor-administered cognitive tests, thereby reducing test time while maintaining diagnostic accuracy through quantitative movement analysis.
Solution Approach 2:
The system enables self-administered testing where the test subject performs hand movements independently while the sensor device automatically collects and analyzes data. This eliminates the need for doctor involvement during testing, reducing operational complexity and test time while preserving measurement precision through automated feature quantity calculation.
2Measurement precision
If brain image measurement such as CT, MRI, SPECT, or PET is used, then diagnostic accuracy is improved, but test cost and test time increase
Solution Approach 1:
The patent employs a simple, low-cost sensor device that can be easily deployed and disposed of or reused, replacing expensive and complex brain imaging equipment. The sensor-based hand movement measurement provides sufficient diagnostic information at a fraction of the cost and complexity of CT, MRI, SPECT, or PET scans.
Solution Approach 2:
The patent extracts the essential diagnostic information from complex brain imaging procedures by focusing specifically on hand movement characteristics. Instead of using full-scale brain imaging, the system isolates and measures specific movement parameters that correlate with cognitive function, thereby reducing device complexity and test cost.
3Measurement precision
If conventional cognitive function tests are used, then diagnostic capability is improved, but ease of operation for large-scale screening deteriorates
Solution Approach 1:
The test subject independently performs hand movements while the sensor device automatically records and analyzes data without requiring doctor administration. This self-service approach makes the test easy to administer to large numbers of people simultaneously, greatly improving ease of operation for mass screening while preserving diagnostic capability through automated analysis.
Solution Approach 2:
The system uses simple sensor copies of hand movement data rather than requiring complex cognitive test administration. By capturing movement patterns through sensors and analyzing feature quantities, the system replicates diagnostic capability in a simplified format that is easily operable for large-scale screening programs.
Data Source
AI summary
A system for evaluating a degree of brain dysfunction of a subject, such as a decline in cognitive function, calculates a difference in movement functions between both hands in a coordination movement. The system includes a storage device for storing time-series data on a finger movement task of each of both hands of a test subject acquired by a movement sensor; a data processing device for analyzing the time-series data stored in the storage device; and a display device for displaying an analysis result analyzed by the data processing device. The data processing device includes a movement waveform generation unit for generating a movement waveform corresponding to the time-series data stored in the storage device; and a difference-between-hands feature quantity generation unit for generating a difference-between-hands feature quantity which represents a difference in respective finger movement tasks between both hands, based on the respective movement waveforms of both hands.