Insole Graph Theory for Parkinson's Detection
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Solution Overview
Problem
Conventional methods for detecting Parkinson's disease progression are limited by their reliance on symptom manifestations and univariate gait features, which provide low accuracy in early detection and are primarily effective in advanced stages of the disease.
Innovation Solution
A method and system utilizing a graph theory approach with Vertical Ground Reaction Force (VGRF) data from pressure sensors embedded in an insole to calculate a mediolateral stability index based on betweenness centrality, enabling the detection of Parkinson's disease intensity by comparing this index with predetermined values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If symptom-based approaches are used to detect Parkinson's disease, then the method is simple to implement, but the accuracy in early detection is low
Solution Approach 1:
The patent segments the foot into multiple pressure zones with individual sensors, transforming a simple symptom check into a multi-point gait analysis system. This segmentation enables detection of subtle gait abnormalities in early PD stages while maintaining systematic ease of implementation through modular sensor placement.
Solution Approach 2:
The patent transitions from univariate gait feature analysis to multivariate analysis by incorporating spatial distribution of pressure across multiple foot zones. This dimensional expansion from single metric to multi-dimensional pressure mapping significantly improves early detection accuracy while preserving implementation feasibility through standard sensor technology.
2Ease of operation
If univariate gait features like stride time are used to monitor Parkinson's disease progression, then the measurement is simple, but the ability to monitor progression effectively is limited
Solution Approach 1:
The patent merges multiple pressure sensor readings from different foot zones into a comprehensive gait analysis framework. By combining spatial pressure distribution data with temporal gait parameters, the system achieves reliable progression monitoring while maintaining operational simplicity through integrated measurement protocols.
Solution Approach 2:
The patent creates a multi-functional measurement system that simultaneously captures static foot pressure distribution and dynamic gait parameters. This universal approach enables both early detection and progression monitoring using the same sensor array, eliminating the need for separate complex measurement procedures.
3Device complexity
If standard gait metrics like stride length are analyzed using insole pressure sensors, then the device complexity is reduced, but the detection capability is profound only in advanced stages
Solution Approach 1:
The patent applies local quality analysis by examining pressure characteristics in specific foot zones (heel, midfoot, forefoot) rather than treating the foot as a single unit. This localized pressure analysis detects subtle gait abnormalities in early PD stages, maintaining device simplicity while significantly improving detection precision through zone-specific metric extraction.
Data Source
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AI summary
This disclosure relates generally to a Parkinson's disease detection system. Parkinson's disease is a neuro-degenerative disorder affecting motor and cognitive functions of subjects. Since symptom manifestation is limited in Parkinson's disease, identifying Parkinson's disease in the early stage is a challenging task. The present disclosure overcomes the limitations of the conventional methods for detecting Parkinson's disease by utilizing a graph theory approach. Here, each pressure sensor attached to an insole corresponding to a plurality of pressure points associated with a foot of the subject is considered as a node of a connectivity graph. The foot dynamics analysis is performed based on a metric known as mediolateral stability index and the mediolateral stability index is calculated by utilizing a betweenness centrality associated with each node of the connectivity graph. Further, the mediolateral stability index is compared with standard values to detect the intensity of the Parkinson's disease.