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September 10, 2025Frontiers in Aging NeuroscienceOpen Access

Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment

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Authors

HGHong GuoFMFengju MaoMZMingming Zhang

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Overview

Cross-sectional study reveals gait parameters predict cognitive impairment in PD patients, suggesting the utility of machine learning methods for enhanced accuracy.

Key Points

  • Logistic regression demonstrated an impressive predictive performance with an AUC of 0.957 for cognitive impairment in PD patients.
  • Seven independent risk factors for cognitive impairment were identified, including gait parameters such as step length and walking speed.
  • Machine learning methods improved prediction accuracy, making gait analysis a promising tool for early identification of cognitive decline.
  • Significant correlations were found between deteriorating gait parameters and lower cognitive scores, highlighting their potential as biomarkers.

Cite This Study

Guo et al. (2025) studied this question.

synapsesocial.com/papers/68c1afc654b1d3bfb60e7ae8https://doi.org/10.3389/fnagi.2025.1590224
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Also Consider

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

  1. 1Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study.2026
  2. 2Objective assessment of gait and posture symptoms in Parkinson’s disease using wearable sensors and machine learning2025 · 2 citations
  3. 3Sensor-Derived Parameters from Standardized Walking Tasks Can Support the Identification of Patients with Parkinson’s Disease at Risk of Gait Deterioration2026
  4. 4Recurrent neural network model of gait can predict Parkinson’s disease2025
  5. 5Identifying Parkinson’s Disease from Gait Biomechanics Using a Participant-Level Machine Learning Analysis Pipeline2026