Tian Yuanyuan, Lin Sen, Xu Hejun, Chen Guangchong
School of Civil Engineering and Architecture, Wuyi University, Jiangmen 529020, China.
School of Business, East China University of Science and Technology, Shanghai 200231, China.
Sensors (Basel). 2024 Nov 22;24(23):7455. doi: 10.3390/s24237455.
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.
在全球范围内，监测建筑工人的生产力、职业健康和安全长期以来一直是一个重大问题。为了解决这个问题，迫切需要有效的方法来持续监测建筑工地并及时识别工人的行为。最近，电子技术和姿态估计算法的进步使得获取人体骨骼和关节轨迹变得更加容易。深度学习算法已成为用于提取和处理建筑工地上3D骨骼信息的强大且自动化的工具，被证明对劳动力行动评估有效。然而，以前大多数关于动作识别的研究主要集中在单流数据上，这限制了网络捕捉更全面的工人动作特征的能力。因此，本研究提出了一种时空多特征网络（STMF-Net），旨在利用基于6个3D骨骼的特征来监测和捕捉建筑工人的动作，从而识别他们的行为。实验结果表明准确率为79.36%。这项工作的意义在于它有潜力增强建筑行业内的管理模式，最终改善工人的健康和工作效率。
