文献类型：期刊文献

英文题名：A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition

作者：Tian, Yuanyuan[1];Lin, Sen[2];Xu, Hejun[3];Chen, Guangchong[4]

机构：[1]Wuyi Univ, Sch Civil Engn & Architecture, Jiangmen 529020, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, Shanghai 200231, Peoples R China;[3]Jiangsu Univ Sci & Technol, Sch Civil Engn & Architecture, Zhenjiang 212100, Peoples R China;[4]Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China

年份：2024

卷号：24

期号：23

外文期刊名：SENSORS

收录：;EI(收录号：20245117531410);WOS:【SCI-EXPANDED(收录号:WOS:001377858400001)】；

基金：This research was funded by Wuyi University, grant number BSQD2409, title: "Doctoral Research Start-up Fund of Wuyi University: BSQD2409".

语种：英文

外文关键词：construction worker; action recognition; 3D skeleton; deep learning algorithm

摘要：Globally, monitoring productivity, occupational health, and safety of construction workers has long been a significant concern. To address this issue, there is an urgent need for efficient methods to continuously monitor construction sites and recognize workers' actions in a timely manner. Recently, advances in electronic technology and pose estimation algorithms have made it easier to obtain skeleton and joint trajectories of human bodies. Deep learning algorithms have emerged as robust and automated tools for extracting and processing 3D skeleton information on construction sites, proving effective for workforce action assessment. However, most previous studies on action recognition have primarily focused on single-stream data, which limited the network's ability to capture more comprehensive worker action features. Therefore, this research proposes a Spatial-Temporal Multi-Feature Network (STMF-Net) designed to utilize six 3D skeleton-based features to monitor and capture the movements of construction workers, thereby recognizing their actions. The experimental results demonstrate an accuracy of 79.36%. The significance of this work lies in its potential to enhance management models within the construction industry, ultimately improving workers' health and work efficiency.