Skeleton-based Human Action Evaluation Using Graph Convolutional Network for Monitoring Alzheimer's Progression

Abstract

Human action evaluation (HAE) involves judgments about the abnormality and quality of human actions. If performed effectively, HAE based on skeleton data can be used to monitor the outcomes of behavioral therapies for Alzheimer’s disease (AD). In this paper, we propose a two-task graph convolutional network (2T-GCN) to represent skeleton data for HAE tasks involving abnormality detection and quality evaluation. The network is first evaluated using the UI-PRMD dataset and demonstrates accurate abnormality detection. Regarding quality evaluation, in addition to laboratory-collected UI-PRMD data, we test the network on a set of real exercise data collected from patients with AD. A numerical score indicating the degree to which actions deviate from normal is taken to reflect the severity of AD; thus, we apply 2T-GCN to determine such scores. Experimental results show that numerical scores for certain exercises performed by patients with AD are consistent with their AD severity level as identified by clinical staff. This corroboration highlights the potential of our approach for monitoring AD and other neurodegenerative diseases.