APSIPA Transactions on Signal and Information Processing > Vol 12 > Issue 5

Plantar Space-Gait Cycle Transformer for Early Parkinson Disease Detection

Xiaoyue Wang, AHU-IAI AI Joint Laboratory, Anhui University, and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China, Teng Li, AHU-IAI AI Joint Laboratory, Anhui University, China, Haoqiang Hua, South China University of Technology, China, Lin Shu, South China University of Technology, China, Xiaofen Xing, South China University of Technology, China, xfxing@scut.edu.cn
 
Suggested Citation
Xiaoyue Wang, Teng Li, Haoqiang Hua, Lin Shu and Xiaofen Xing (2023), "Plantar Space-Gait Cycle Transformer for Early Parkinson Disease Detection", APSIPA Transactions on Signal and Information Processing: Vol. 12: No. 5, e202. http://dx.doi.org/10.1561/116.00000227

Publication Date: 21 Nov 2023
© 2023 X. Wang, T. Li, H. Hua, L. Shu and X. Xing
 
Subjects
 
Keywords
ParkinsonTransformerSelf-AttentionPlantar PressureVGRF
 

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In this article:
Introduction 
Relate Work 
Methodology 
Experiments 
Discussion 
Conclusion and Future Work 
References 

Abstract

Parkinson’s disease (PD) is a chronic and long-term disease that seriously affects patients’ quality of life. In underdeveloped areas, early detection of PD is primarily based on medical observation and patient self-description. Early diagnosis of PD can effectively reduce the disease’s progression. Recent studies have suggested that the motor symptoms of PD can be reflected in plantar pressure. However, traditional machine learning models require manual feature selection, which can be time-consuming. Furthermore, although deep learning has seen rapid development, many clinical characteristics have not been taken into consideration. To address these limitations, a dual self-attention Transformer model is proposed to explore the spatial correlation of plantar space and the temporal correlation of the gait cycle. Considering the presence of symptoms such as foot tremors in PD patients, a masking mechanism is designed to focus locally on the unilateral foot during the support phase. An experimental paradigm is designed to evaluate the model’s generalization capability across different subjects. The experimental results demonstrate that the proposed model achieves superior classification performance for the early detection of PD based on plantar pressure data.

DOI:10.1561/116.00000227

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