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Title of Paper:Adaptive Spatio-Temporal Directed Graph Neural Network for Parkinson's Detection using Vertical Ground Reaction Force.

Journal:In Proceedings of the 31st ACM International Conference on Multimedia

Key Words:Parkinson’s Disease Detection, Topological Graph, Vertical Ground Reaction Force, Graph Neural Network

Summary:Vertical Ground Reaction Force (VGRF) signal obtained from footworn sensors, also known as plantar data, provides a highly informative and detailed representation of an individual’s gait features. Existing methods, such as CNNs, LSTMs and Transformers, have revealed the efficiency of deep learning in Parkinson’s Disease (PD) diagnosis using VGRF signal. However, the intrinsic topologic graph and pressure transmission characteristics of plantar data are overlooked in those approaches, which are essential features for gait analysis. In this paper, we propose to construct a plantar directed topologic graph to fully exploit the plantar topology in gait circles. It can facilitate the expression of gait information by representing sensors as nodes and pressure transmissions as directional edges. Accordingly, an Adaptive Spatio-Temporal Directed Graph Neural Network (AST-DGNN) is proposed to extract the connection features of the plantar directed topologic graph. Each AST-DGNN Unit includes an Adaptive Directed Graph Network (ADGN) block and a Temporal Convolutional Network (TCN) block. In order to capture both local and global spatial relationships among sensor nodes and pressure transmission edges, the ADGN block performs message passing on the plantar directed topologic graph in an adaptive manner. To capture the temporal features of sensor nodes and pressure transmission edges, the TCN block defines a temporal feature extraction process for each node and edge in the graph. Moreover, the data augmentation is introduced for plantar data to improve the generalization ability of the AST-DGNN. Experimental results on Ga, Ju, and Si datasets demonstrate that the proposed method outperforms the existing methods under both cross-dataset validation and mixed-data cross-validation. Especially in cross-dataset validation, there is an average improvement of 2.13%, 7.73%, and 12.27% in accuracy, F1 score, and G-mean, respectively.

All the Authors:Shuo Liang,Zhifu Zhao,Xinyu Cui,Kai Chen,Xuanhang Xu

First Author:Xiaotian Wang

Correspondence Author:Zhifu Zhao

Document Type:C

Page Number:8740-8748 (CCFA类)

Translation or Not:No

Date of Publication:2023-10-29

Date:2025-12-16