Vision-based human action quality assessment: A systematic review

Liu, Jiang, Wang, Huasheng, Stawarz, Katarzyna ORCID: https://orcid.org/0000-0001-9021-0615, Li, Shiyin, Fu, Yao and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2025. Vision-based human action quality assessment: A systematic review. Expert Systems with Applications 263 , 125642. 10.1016/j.eswa.2024.125642

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Abstract

Human Action Quality Assessment (AQA), which aims to automatically evaluate the performance of actions executed by humans, is an emerging field of human action analysis. Although many review articles have been conducted for human action analysis fields such as action recognition and action prediction, there is a lack of up-to-date and systematic reviews related to AQA. This paper aims to provide a systematic literature review of existing papers on vision-based human AQA. This systematic review was rigorously conducted following the PRISMA guideline through the databases of Scopus, IEEE Xplore, and Web of Science in July 2024. Ninety-six research articles were selected for the final analysis after applying inclusion and exclusion criteria. This review presents an overview of various aspects of AQA, including existing applications, data acquisition methods, public datasets, state-of-the-art methods and evaluation metrics. We observe an increase in the number of studies in AQA since 2019, primarily due to the advent of deep learning methods and motion capture devices. We categorize these AQA methods into skeleton-based and video-based methods based on the data modality used. There are different evaluation metrics for various AQA tasks. SRC is the most commonly used evaluation metric, with fifty-six out of ninety-six selected papers using it to evaluate their models. Sports event scoring, surgical skill evaluation and rehabilitation assessment are the most popular three scenarios in this direction based on existing papers and there are more new scenarios being explored such as piano skill assessment. Furthermore, the existing challenges and future research directions are provided, which can be a helpful guide for researchers to explore AQA.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0957-4174
Date of First Compliant Deposit:	28 November 2024
Date of Acceptance:	26 October 2024
Last Modified:	28 Nov 2024 12:45
URI:	https://orca.cardiff.ac.uk/id/eprint/174331

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