Spatio-Temporal Pyramid Graph Convolutions for Human Action Recognition and Postural Assessment
This work addresses postural assessment for occupational safety, offering an incremental improvement by integrating online action recognition with traditional ergonomic risk indices.
The paper tackled human action recognition for ergonomic risk assessment by proposing a Spatio-Temporal Pyramid Graph Convolutional Network (ST-PGN), which outperformed state-of-the-art methods on two public benchmark datasets (TUM and UW-IOM).
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.