Sparse Adaptive Graph Convolutional Network for Leg Agility Assessment in Parkinson's Disease
| Autor: | Xiangxin Shao, Chencheng Zhang, Rui Guo, Xiaohua Qian |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
Databases Factual Computer science media_common.quotation_subject Feature extraction Biomedical Engineering Video Recording Feature selection 02 engineering and technology Machine learning computer.software_genre Machine Learning 03 medical and health sciences Automation 0302 clinical medicine Discriminative model Rating scale Perception 0202 electrical engineering electronic engineering information engineering Internal Medicine Humans media_common Aged Leg Movement Disorders business.industry General Neuroscience Rehabilitation COVID-19 Reproducibility of Results Parkinson Disease Middle Aged Telemedicine Salient Task analysis Graph (abstract data type) 020201 artificial intelligence & image processing Female Artificial intelligence Neural Networks Computer business computer 030217 neurology & neurosurgery Algorithms Psychomotor Performance |
| Zdroj: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| ISSN: | 1558-0210 |
| Popis: | Motor disorder is a typical symptom of Parkinson’s disease (PD). Neurologists assess the severity of PD motor symptoms using the clinical rating scale, i.e., MDS-UPDRS. However, this assessment method is time-consuming and easily affected by the perception difference of assessors. In the recent outbreak of coronavirus disease 2019, telemedicine for PD has become extremely urgent for clinical practice. To solve these problems, we developed an automated and objective assessment method of the leg agility task in the MDS-UPDRS using videos and a graph neural network. In this study, a sparse adaptive graph convolutional network (SA-GCN) was proposed to achieve fine-grained quantitative assessment of skeleton sequences extracted from videos. Specifically, the sparse adaptive graph convolutional unit with a prior knowledge constraint was proposed to perform adaptive spatial modeling of physical and logical dependency for skeleton sequences, thus achieving the sparse modeling of the discriminative spatial relationships. Subsequently, a temporal context module was introduced to construct the remote context dependency in the temporal dimension, hence determining the global changes of the task. A multi-domain attention learning module was also developed to integrate the static spatial features and dynamic temporal features, and then to emphasize the salient feature selection in the channel domain, thereby capturing the multi-domain fine-grained information. Finally, the evaluation results using a dataset with 148 patients and 870 samples confirmed the effectiveness and reliability of our scheme, and the method outperformed other related state-of-the-art methods. Our contactless method provides a new potential tool for automated PD assessment and telemedicine. |
| Databáze: | OpenAIRE |
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