Encoder-Decoder Graph Convolutional Network for Automatic Timed-Up-and-Go and Sit-to-Stand Segmentation

Vision-based action segmentation is an important tool in human movement analysis. In this work, we present a novel Encoder-Decoder Graph Convolutional Network (ED-GCN) to perform auto-segmentation on two widely accepted clinical tests for human mobility and balance assessment: the "Timed-Up-and-Go" (TUG) test and the "Sit-to-Stand" (STS) test. For STS, we perform a fine-grained segmentation that further segments the stand up and sit down actions into more sub-phases. To the best of our knowledge, this is the first work that analyzes such subtle segmentation with biomedical significance. We also propose two novel metrics for action segmentation, which overcome some key drawbacks in the popular F1 and Edit scores. Experiment shows that our network has superior performance over state-of-the-art action segmentation networks in TUG and STS segmentation.