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November 13, 2025TechnologiesOpen Access

A Parkinson’s Disease Recognition Method Based on Plantar Pressure Feature Fusion

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Authors

LMLan MaHHHua Huo

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Overview

This model detects Parkinson’s disease in gait analysis, suggesting its potential for accurate diagnosis using foot pressure data.

Key Points

  • The aim is to develop a model for early detection of Parkinson's disease using foot pressure data.
  • Utilized a deep learning model to analyze pressure data during walking.
  • Addressed spatial irregularity and data disorder with a Transformer-based attention mechanism.
  • Employed tensor fusion technique for integrating foot features from various datasets.
  • Achieved 87.03% accuracy in detecting Parkinson’s disease.
  • Demonstrated good stability in differentiating patients from healthy individuals.

Cite This Study

Ma et al. (2025) studied this question.

synapsesocial.com/papers/692523cec0ce034ddc355158https://doi.org/10.3390/technologies13110522
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A comprehensive survey on diagnosis and assessment of Parkinson’s disease via plantar pressure analysis2026
  2. 2Detection of freezing of gait in Parkinson's disease from foot-pressure sensing insoles using a temporal convolutional neural network2024 · 23 citations
  3. 3Deep Convolutional Neural Network-Based Detection of Gait Abnormalities in Parkinson’s Disease Using Fewer Plantar Sensors in a Smart Insole2026 · 1 citations
  4. 4A Minimalist, Foot-Mounted IMU Approach to Parkinson’s Disease Detection in Semi-Controlled Settings2026
  5. 5A gait recognition architecture for early screening in the assessment of Parkinson’s patients2025