Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression

Highlights：

• We propose a novel two-task graph convolutional network (2T-GCN) to represent skeleton data for human action evaluation (HAE) tasks of abnormality detection and quality evaluation. To the best of our knowledge, this is the first work that applies GCN to skeleton-based HAE.

• We validate the effectiveness of our 2T-GCN on a public dataset, UI-PRMD. Results show that our method outperforms existing methods. Additionally, the Kinect v2 sensor may be more capable of HAE tasks than the Vicon optical tracking system.

• We use Kinect v2 to collect an exercise dataset from subjects with Alzheimer’s disease (AD). Based on experiments, findings show that our method can be effective for HAE tasks. We also observe that the evaluation scores for some exercises coincide with clinical evaluations of AD.