A novel plantar pressure analysis method to signify gait dynamics in Parkinson's disease

Yubo Sun; Yuanyuan Cheng; Yugen You; Yue Wang; Zhizhong Zhu; Yang Yu; Jianda Han; Jialing Wu; Ningbo Yu; Yubo Sun; Yuanyuan Cheng; Yugen You; Yue Wang; Zhizhong Zhu; Yang Yu; Jianda Han; Jialing Wu; Ningbo Yu

doi:10.3934/mbe.2023601

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 13474-13490. doi: 10.3934/mbe.2023601

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A novel plantar pressure analysis method to signify gait dynamics in Parkinson's disease

1.
College of Artificial Intelligence, Nankai University, Tianjin 300350, China
2.
Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China
3.
Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300070, China
4.
Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
5.
Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
6.
Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China
7.
Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China

Academic Editor: Mariano Torrisi
† These authors contributed equally to this work.

Received: 17 March 2023 Revised: 29 May 2023 Accepted: 05 June 2023 Published: 13 June 2023

Plantar pressure can signify the gait performance of patients with Parkinson's disease (PD). This study proposed a plantar pressure analysis method with the dynamics feature of the sub-regions plantar pressure signals. Specifically, each side's plantar pressure signals were divided into five sub-regions. Moreover, a dynamics feature extractor (DFE) was designed to extract features of the sub-regions signals. The radial basis function neural network (RBFNN) was used to learn and store gait dynamics. And a classification mechanism based on the output error in RBFNN was proposed. The classification accuracy of the proposed method achieved 100.00% in PD diagnosis and 95.89% in severity assessment on the online dataset, and 96.00% in severity assessment on our dataset. The experimental results suggested that the proposed method had the capability to signify the gait dynamics of PD patients.
- gait disturbance,
- plantar pressure,
- sub-regions division,
- RBFNN,
- severity assessment
Citation: Yubo Sun, Yuanyuan Cheng, Yugen You, Yue Wang, Zhizhong Zhu, Yang Yu, Jianda Han, Jialing Wu, Ningbo Yu. A novel plantar pressure analysis method to signify gait dynamics in Parkinson's disease[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 13474-13490. doi: 10.3934/mbe.2023601

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Abstract

Plantar pressure can signify the gait performance of patients with Parkinson's disease (PD). This study proposed a plantar pressure analysis method with the dynamics feature of the sub-regions plantar pressure signals. Specifically, each side's plantar pressure signals were divided into five sub-regions. Moreover, a dynamics feature extractor (DFE) was designed to extract features of the sub-regions signals. The radial basis function neural network (RBFNN) was used to learn and store gait dynamics. And a classification mechanism based on the output error in RBFNN was proposed. The classification accuracy of the proposed method achieved 100.00% in PD diagnosis and 95.89% in severity assessment on the online dataset, and 96.00% in severity assessment on our dataset. The experimental results suggested that the proposed method had the capability to signify the gait dynamics of PD patients.

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