Open Access
ARTICLE
VMHPE: Human Pose Estimation for Virtual Maintenance Tasks
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, China
* Corresponding Author: Yueming Wu. Email:
Computers, Materials & Continua 2025, 85(1), 801-826. https://doi.org/10.32604/cmc.2025.066540
Received 10 April 2025; Accepted 18 June 2025; Issue published 29 August 2025
Abstract
Virtual maintenance, as an important means of industrial training and education, places strict requirements on the accuracy of participant pose perception and assessment of motion standardization. However, existing research mainly focuses on human pose estimation in general scenarios, lacking specialized solutions for maintenance scenarios. This paper proposes a virtual maintenance human pose estimation method based on multi-scale feature enhancement (VMHPE), which integrates adaptive input feature enhancement, multi-scale feature correction for improved expression of fine movements and complex poses, and multi-scale feature fusion to enhance keypoint localization accuracy. Meanwhile, this study constructs the first virtual maintenance-specific human keypoint dataset (VMHKP), which records standard action sequences of professional maintenance personnel in five typical maintenance tasks and provides a reliable benchmark for evaluating operator motion standardization. The dataset is publicly available at . Using high-precision keypoint prediction results, an action assessment system utilizing topological structure similarity was established. Experiments show that our method achieves significant performance improvements: average precision (AP) reaches 94.4%, an increase of 2.3 percentage points over baseline methods; average recall (AR) reaches 95.6%, an increase of 1.3 percentage points. This research establishes a scientific four-level evaluation standard based on comparative motion analysis and provides a reliable solution for standardizing industrial maintenance training.Keywords
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Copyright © 2025 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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