Title | ||
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A Self-Supervised Metric Learning Framework for the Arising-From-Chair Assessment of Parkinsonians With Graph Convolutional Networks |
Abstract | ||
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The onset and progression of Parkinson’s disease (PD) gradually affect the patient’s motor functions and quality of life. The PD motor symptoms are usually assessed using the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Automated MDS-UPDRS assessment has been recently required as an invaluable tool for PD diagnosis and telemedicine, especially with the recent novel coronavirus pandemic outbreak. This paper proposes a novel vision-based method for automated assessment of the arising-from-chair task, which is one of the key MDS-UPDRS components. The proposed method is based on a self-supervised metric learning scheme with a graph convolutional network (SSM-GCN). Specifically, for human skeleton sequences extracted from videos, a self-supervised intra-video quadruplet learning strategy is proposed to construct a metric learning formulation with prior knowledge, for improving the spatial-temporal representations. Afterwards, a vertex-specific convolution operation is designed to achieve effective aggregation of all skeletal joint features, where each joint or feature is weighted differently based on its relative factor of importance. Finally, a graph representation supervised mechanism is developed to maximize the potential consistency between the joint and bone information streams. Experimental results on a clinical dataset demonstrate the superiority of the proposed method over the existing sensor-based methods, with an accuracy of 70.60% and an acceptable accuracy of 98.65%. The analysis of discriminative spatial connections makes our predictions more clinically interpretable. This method can achieve reliable automated PD assessment using only easily-obtainable videos, thus providing an effective tool for real-time PD diagnosis or remote continuous monitoring. |
Year | DOI | Venue |
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2022 | 10.1109/TCSVT.2022.3163959 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Parkinson’s disease,video-based assessment,self-supervised learning,metric learning,graph convolutional network | Journal | 32 |
Issue | ISSN | Citations |
9 | 1051-8215 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Rui Guo | 1 | 0 | 1.01 |
Jie Sun | 2 | 0 | 0.34 |
Chencheng Zhang | 3 | 16 | 2.48 |
Xiaohua Qian | 4 | 4 | 3.78 |