ORIGINAL RESEARCH article

Front. Neurol.

Sec. Artificial Intelligence in Neurology

Volume 16 - 2025 | doi: 10.3389/fneur.2025.1567344

This article is part of the Research TopicModeling Physical Activities, Behavioral Patterns, and Symptoms in Aging and Neurological Disorders via Novel Sensing and AI TechniquesView all 7 articles

Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks

Provisionally accepted
  • 1Institute of Software, Chinese Academy of Sciences (CAS), Beijing, China
  • 2School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
  • 3Chinese Academy of Medical Sciences and Peking Union Medical College, Dongcheng, Beijing Municipality, China
  • 4University of Science and Technology Beijing, Beijing, Beijing Municipality, China
  • 5Health Service Department of the Guard Bureau of the Joint Staff Department, Beijing, China

The final, formatted version of the article will be published soon.

Introdcution: Parkinson's Disease (PD) is a progressive neurodegenerative disorder that primarily impacts motor function and is prevalent among older adults worldwide. Gait performance (such as speed, stride, step, and so on) has been shown to play a significant role in diagnosis, treatment, and rehabilitation. Fortunately, advancements in computer science have provided serial ways to calculate gait-related parameters, offering a more accurate alternative to the complex and often imprecise assessments traditionally relied upon by trained professionals. However, most of the current methods depend on data preprocessing and feature engineering, often require domain knowledge and laborious human involvement, and require additional manual adjustments when dealing with new tasks.To reduce the model's reliance on data preprocessing, feature engineering, and traversal rules, we employed the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model. We also defined five distinct states within a complete gait cycle: standstill (S), left swing (L), double support (D), right swing (R), and turnaround (T). Using ST-GCN, we extracted spatial and temporal patterns from these five states directly from the data, thereby enhancing the accuracy of gait parameter calculation. Furthermore, to improve the interpretability of the ST-GCN model and increase its clinical relevance, we trained the model on data from both healthy individuals and PD patients. This allowed us to explore how the model's parameters (different ST-GCN Layers) could assist clinicians in understanding.The dataset used to evaluate the model in this paper includes motion data from 65 PD participants and 77 healthy control participants. Regarding the classification results from the 5 classifiers, ST-GCN achieved an average precision, recall, and F1-score of 93.48%, 93.21%, 1 Wu et al.and 93.32%, outperforming both the Transformer-based and LSTM-based methods. Displaying the joints and edge weights from various layers of the ST-GCN, particularly when comparing data from healthy individuals and PD patients, enhances the model's feasibility and offers greater interpretability. This approach is more informative than relying on a purely black-box model.This study demonstrated that the ST-GCN approach can effectively support accurate gait parameter assessment, assisting medical professionals in making diagnoses and reasonable rehabilitation plans for patients with PD.

Keywords: Parkinson's disease, gait analysis, Quantitative assessment, Graph convolutional network, skeleton-based data

Received: 27 Jan 2025; Accepted: 05 Jun 2025.

Copyright: © 2025 Wu, Su, Li, Yao, Zhang, Zhang and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Xucheng Zhang, Institute of Software, Chinese Academy of Sciences (CAS), Beijing, China
Wei Sun, Institute of Software, Chinese Academy of Sciences (CAS), Beijing, China

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