Affiliation:
1. School of Cyberspace Security, Changzhou College of Information Technology, Changzhou 213000, China
2. School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213000, China
Abstract
Accurate diagnosis of Parkinson’s disease (PD) is challenging in clinical medicine. To reduce the diagnosis time and decrease the diagnosis difficulty, we constructed a two-stream Three-Dimensional Convolutional Neural Network (3D-CNN) based on pressure sensor data. The algorithm considers the stitched surface of the feet as an “image”; the geometric positions of the pressure sensors are considered as the “pixel coordinates” and combines the time dimension to form 3D data. The 3D-CNN is used to extract the spatio-temporal features of the gait. In addition, a twin network of 3D-CNN with shared parameters is used to extract the spatio-temporal features of the left and right foot respectively to further obtain symmetry information, which not only extracts the spatial information between the multiple sensors but also obtains the symmetry features of the left and right feet at different spatio-temporal locations. The results show that the proposed model is superior to other advanced methods. Among them, the average accuracy of Parkinson’s disease diagnosis is 99.07%, and the average accuracy of PD severity assessment is 98.02%.
Funder
eneral Project of Higher Education Reform Research in Jiangsu Province
“Industrial Internet Solutions and Security Protection Technology Project” from Changzhou College of Information Technology
Scientific and technological innovation team of “predictive maintenance and innovative application of industrial Internet” from Changzhou College of Information Technology
outstanding young teacher of “Qinglan Project” in colleges and universities from Jiangsu Provincial Department of Education funded project
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering