# Spatio-Temporal Pyramid Graph Convolutions for Human Action Recognition and Postural Assessment

arXiv: https://arxiv.org/abs/1912.03442 · HTML: https://mlsanity.com/paper/1912.03442/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2019.

## Built on by (most cited first, 9 of 9)

- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition](https://mlsanity.com/paper/2406.13327/) (2024)
- [Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment](https://mlsanity.com/paper/2305.19478/) (2023)
- [Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2209.10073/) (2022)
- [Understanding Spatio-Temporal Relations in Human-Object Interaction using Pyramid Graph Convolutional Network](https://mlsanity.com/paper/2410.07912/) (2024)
- [Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion](https://mlsanity.com/paper/2309.06462/) (2023)
- [Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation](https://mlsanity.com/paper/2506.11774/) (2025)
- [DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition](https://mlsanity.com/paper/2512.11941/) (2025)
- [Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization](https://mlsanity.com/paper/2604.15196/) (2026)

## Abstract (arXiv)

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.