Multi-Axial Parkinson's Symptom Detection via Frequency Segmentation
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Solution Overview
Problem
Current systems for monitoring Parkinson's disease and extrapyramidal symptoms struggle to accurately distinguish between motor symptoms and normal daily activities, often confusing frequency ranges and overlapping symptom patterns, leading to inaccurate results.
Innovation Solution
A multi-axial measuring device with a processor that performs frequency analysis and spectral processing to identify specific frequency content and movement patterns, comparing these to reference patterns to accurately differentiate between motor symptoms and voluntary movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency analysis is performed to detect motor symptoms in specific frequency ranges, then motor symptoms can be detected, but normal daily activities are incorrectly identified as motor symptoms due to overlapping frequency ranges
Solution Approach 1:
The patent segments the frequency analysis by performing separate analyses for each axis of the multi-axial measuring device, then combines the results. This segmentation allows differentiation of movement patterns along different spatial dimensions, enabling distinction between pathological tremors and normal activities even when they occupy similar frequency ranges.
Solution Approach 2:
The patent transitions from single-axis to multi-axial measurement, adding spatial dimensionality to the detection system. By analyzing frequency content across multiple axes simultaneously and comparing their relationships, the system can distinguish between the characteristic patterns of motor symptoms and normal activities, resolving the ambiguity caused by frequency overlap.
2Duration of action of moving object
If continuous monitoring is implemented to track symptom fluctuations throughout the day, then real-time symptom assessment is achieved, but the system cannot distinguish between voluntary movements and involuntary motor symptoms
Solution Approach 1:
The patent segments the continuous movement data into discrete axis components, analyzing frequency content independently for each axis before integration. This segmentation reveals characteristic patterns of involuntary movements that differ from voluntary actions in their multi-axis frequency distribution, enabling accurate differentiation during continuous monitoring.
Solution Approach 2:
The patent performs frequency analysis on each individual axis (partial action) rather than analyzing the aggregate movement signal. This partial analysis approach provides sufficient information to distinguish between movement types without requiring excessive processing of the complete multi-dimensional signal, maintaining precision during continuous operation.
3Measurement precision
If spectral analysis is performed to quantify symptom severity, then objective measurement is achieved, but the complexity of processing multi-axis frequency data increases
Solution Approach 1:
The patent divides the complex multi-axis spectral analysis into separate frequency analyses for each axis, then combines the results through systematic comparison. This segmentation reduces the computational complexity of processing the complete multi-dimensional signal while preserving the accuracy needed for symptom quantification.
Data Source
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AI summary
The present invention refers to a method, and a related device, which determines a kinetic state of a subject and includes the following operations: The determination of a signal indicative of the acceleration trend (ax, ay, az) on the three Cartesian axes (X, Y, Z); - The signal processing to limit the frequency band and preferably reduce artifacts and compensate the offset of the output signals from the multi-axial measurement system; The frequency analysis and spectral analysis through the transformation of the signal with the Fournier transform; The computation of the power spectral density (E) for each axis (Sx, Sy, Sz); And in which a comparison is made between said power spectral density (E) with a characteristic pattern of a movement.