Integrated Equipment for Parkinson's Disease Early Detection Using Graph Convolution Network

There is an increasing need to diagnose Parkinson's disease (PD) in an early stage. Existing solutions mainly focused on traditional ways such as MRI, thus suffering from the ease-of-use issue. This work presents a new approach using video and skeleton-based techniques to solve this problem. In this paper, an end-to-end Parkinson's disease early diagnosis method based on graph convolution networks is proposed, which takes patients' skeletons sequence as input and returns the diagnosis result. The asymmetric dual-branch network architecture is designed to process global and local information separately and capture the subtle manifestation of PD. To train the network, we present the first Parkinson's disease gait dataset, PD-Walk. This dataset consists of 95 PD patients and 96 healthy people's walking videos. All the data are annotated by experienced doctors. Furthermore, we implement our method on portable equipment, which has been in operation in the First Affiliated Hospital, Zhejiang University School of Medicine. Experiments show that our method can achieve 84.1% accuracy and achieve real-time performance on the equipment in the real environment. Compared with traditional solutions, the proposed method can detect suspicious PD symptoms quickly and conveniently. Integrated equipment can be easily placed in hospitals or nursing homes to provide services for elderly people.