Action Quality Assessment (AQA)---the ability to quantify the quality of human motion, actions, or skill levels and provide feedback---has far-reaching implications in areas such as low-cost physiotherapy, sports training, and workforce development. As such, it has become a critical field in computer vision and video understanding over the past decade. Significant progress has been made in AQA methodologies, datasets, and applications, yet a pressing need remains for a comprehensive synthesis of this rapidly evolving field. In this paper, we present a thorough survey of the AQA landscape, systematically reviewing over 200 research papers using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. We begin by covering foundational concepts and definitions, then move to general frameworks and performance metrics, and finally discuss the latest advances in methodologies and datasets. This survey provides a detailed analysis of research trends, performance comparisons, challenges, and future directions. Through this work, we aim to offer a valuable resource for both newcomers and experienced researchers, promoting further exploration and progress in AQA.
@article{yin2025decadeactionqualityassessment,
title={A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions},
author={Hao Yin and Paritosh Parmar and Daoliang Xu and Yang Zhang and Tianyou Zheng and Weiwei Fu},
journal={arXiv preprint arXiv:2502.02817},
year={2025},
}