A Two-Stream 3D-CNN Network Based on Pressure Sensor Data and Its Application in Gait Recognition

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%.