  Recognition of human actions and associated interactions with objects and the
environment is an important problem in computer vision due to its potential
applications in a variety of domains. The most versatile methods can generalize
to various environments and deal with cluttered backgrounds, occlusions, and
viewpoint variations. Among them, methods based on graph convolutional networks
that extract features from the skeleton have demonstrated promising
performance. In this paper, we propose a novel Spatio-Temporal Pyramid Graph
Convolutional Network (ST-PGN) for online action recognition for ergonomic risk
assessment that enables the use of features from all levels of the skeleton
feature hierarchy. The proposed algorithm outperforms state-of-art action
recognition algorithms tested on two public benchmark datasets typically used
for postural assessment (TUM and UW-IOM). We also introduce a pipeline to
enhance postural assessment methods with online action recognition techniques.
Finally, the proposed algorithm is integrated with a traditional ergonomic risk
index (REBA) to demonstrate the potential value for assessment of
musculoskeletal disorders in occupational safety.
