Analysis and Research on Gait Disorder in Parkinson's Disease Based on Deep Learning.

As one of the basic activities of human beings, the analysis of walking is of great significance in clinical research. In this paper, we analyzed the published plantar pressure dataset Gait in Parkinson's Disease, designed a method to divide the gait period, and extracted the characteristic parameters of gait. A hybrid neural network (GRU-DNN) was applied to classify Parkinson's disease by combining the gated circulation unit (GRU) and the deep neural network (DNN). Data analysis provided more objective basis for clinical diagnosis, thereby assisting doctors in diagnosing the disease. To verify the effectiveness of the method, the network was used to classify and predict gait information with disease labels in the dataset. In Parkinson's disease diagnosis experiment, the recognition accuracy of this network was 98.7%. In the Parkinson's severity diagnosis experiment, the network achieved 100% recognition accuracy for severity level 2, and 98% for the rest of the severity level. [ABSTRACT FROM AUTHOR]