Motor Function Estimating System Using Finger Tapping Waveform Analysis
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Solution Overview
Problem
Current methods for diagnosing motion disorders, such as Parkinson's disease, are subjective and fail to accurately estimate disease severity due to individual differences among doctors and the complexity of multiple symptoms, often confusing motor functional decline with aging.
Innovation Solution
A motor function estimating system that uses a motion sensor to capture fingers tapping motion waveforms, analyzes these waveforms to generate characteristics, and synthesizes a motion disorder severity value by comparing them to healthy person data, accounting for aging-related declines in motor function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a doctor performs subjective diagnosis using UPDRS, then the diagnosis can be made with simple methodology, but individual differences occur between doctors and the method is not objective
Solution Approach 1:
The patent replaces the subjective mechanical judgment process of doctors with an automated objective measurement system using motion sensors and computer analysis. The system captures finger tapping motions through sensors and uses algorithms to objectively calculate motor function indicators, eliminating individual differences between doctors while maintaining ease of operation through automated processing.
Solution Approach 2:
The patent introduces motion sensors and computer analysis algorithms as intermediaries between the patient's motor function and the diagnosis result. These intermediaries objectively capture and analyze finger tapping motions, translating physical movements into quantifiable data that removes subjectivity from the diagnostic process while keeping the overall system simple to operate.
2Device complexity
If characteristics from single symptom are used for estimation, then the measurement process is simple, but it is difficult to estimate precisely disease severity when multiple symptoms develop simultaneously
Solution Approach 1:
The patent merges multiple symptom characteristics into a comprehensive evaluation system. It simultaneously measures various aspects of finger tapping motion (amplitude, frequency, rhythm, smoothness) and integrates these multiple characteristics through computer analysis to produce a comprehensive disease severity estimate, enabling precise diagnosis even when multiple symptoms coexist.
Solution Approach 2:
The patent transitions from single-dimension symptom assessment to multi-dimensional analysis by evaluating multiple characteristics of finger tapping motion simultaneously. The system analyzes amplitude, frequency, rhythm, and smoothness as separate dimensions and synthesizes them into a comprehensive three-dimensional assessment of motor function, improving precision without excessive complexity.
3Ease of operation
If subjective decision is made by doctor for senior persons, then the process is simple, but motor functional decline by aging is easily mistaken for motion disorder
Solution Approach 1:
The patent performs preliminary objective measurement and analysis of motor function before making diagnostic decisions. By capturing and analyzing finger tapping motions with motion sensors and calculating objective indicators in advance, the system establishes a baseline that distinguishes age-related decline from pathological changes, preventing misdiagnosis in elderly patients while keeping the process simple.
Solution Approach 2:
The patent implements feedback mechanisms where the computer analysis system continuously compares measured characteristics with established norms and provides objective feedback to clinicians. This feedback loop helps distinguish between normal aging patterns and pathological changes by referencing standardized data, improving diagnostic reliability for elderly patients without complicating the overall process.
Data Source
AI summary
The motor function estimating system 1000 has a memory unit 1260, a data processing unit 1220 (analyzing section), and a display unit 1400. The memory unit 1260 memorizes waveform data about the time change of fingers tapping motion that is obtained by a motion sensor attached to a subject person who does the fingers tapping motion that is repetition of the opening and closing motion of two fingers of one hand. The data processing unit 1220 analyzes the waveform data in the memory unit 1260. The display unit 1400 displays an analysis result. The data processing unit 1220 creates motion waveform based on waveform data, and plural characteristics based on the motion waveform. The data processing unit 1220 creates a motion disorder synthesis value that represents degree of motion disorder of the subject person by revising each of the plural characteristics based on characteristics of every age and synthesizing them.