Static-Dynamic Temporal Networks for Parkinson’s Disease Detection and Severity Prediction

Abstract

Most patients with Parkinson's disease (PD) have different degrees of movement disorders, and effective gait analysis has a huge potential for uncovering hidden gait patterns to achieve the diagnosis of patients with PD. In this paper, the Static-Dynamic temporal networks are proposed for gait analysis. Our model involves a Static temporal pathway and a Dynamic temporal pathway. In the Static temporal pathway, the time series information of each sensor is processed independently with a parallel one-dimension convolutional neural network (1D-Convnet) to extract respective depth features. In the Dynamic temporal pathway, the stitched surface of the feet is deemed to be an irregular "image", and the transfer of the force points at all levels on the sole is regarded as the "optical flow." Then, the motion information of the force points at all levels is extracted by 16 parallel two-dimension convolutional neural network (2D-Convnet) independently. The results show that the Static-Dynamic temporal networks achieved better performance in gait detection of PD patients than other previous methods. Among them, the accuracy of PD diagnosis reached 96.7%, and the accuracy of severity prediction of PD reached 92.3%.

A Comprehensive Review of Deep Learning Techniques for Identifying Parkinson's Disease from Gait Analysis

“…Dong et al [41] developed static-dynamic temporal networks to evaluate PD severity by gait analysis. Initially, the pressure signals are collected by various sensors on the bottom of the foot, and each signal is assigned a distinct depth attribute.…”