###### Abstract  
The safety management of laboratories is of utmost importance in the construction and management of university laboratories. Abnormal behaviors such as smoking, incorrect wearing of personal protective equipment (PPE) like lab coats, hats, masks, and gloves pose significant safety hazards. In this paper, in order to improve the level of laboratory safety management and effectively provide an alert in the case of unsafe behaviors, video analysis technology is employed to achieve abnormal behavior recognition and monitoring through steps such as human key point detection, posture estimation, and behavior recognition. Firstly, the human pose estimation algorithm YOLO is used for human detection, followed by the extraction of human key points after segmentation. Finally, spatiotemporal graph convolution is used for feature detection and classification of abnormal behaviors. The experimental results show that the accuracy of abnormal behavior detection and recognition based on human key points reaches over 85%, which is of great significance for safety management and behavior warning in university laboratories, and thus, improves the efficiency and level of laboratory safety management. video analysis technology; laboratory safety management; human joint points; behavior recognition +
Footnote †: journal:  
videos, providing instant alerts and interventions for unsafe behaviors in laboratories, significantly enhances the digital management of laboratory safety [7]. The primary goal of this research is to enhance laboratory safety management through real-time detection of abnormal behaviors by leveraging advanced video analysis techniques. To achieve this goal, we aim to develop an efficient algorithm that utilizes human pose estimation and behavior recognition to detect abnormal behaviors in real time, focusing on actions such as properly wearing protective equipment and smoking. Human motion analysis is a crucial aspect of understanding human behavior and has broad applications in intelligent security, video surveillance, rehabilitation, and healthcare. Current mainstream deep learning methods for human motion analysis can be categorized into two approaches: image-based and skeleton-based methods. Image-based methods use video frames as input and extract temporal features through techniques like 3D convolution [8], which effectively captures both spatial and temporal features from videos, significantly improving performance in tasks such as action recognition. In addition, long short-term memory networks [9] capture both spatial and long-term temporal dependencies, improving performance in video-based tasks like action recognition and video description. Moreover, optical flow [10] significantly improves performance in action recognition tasks by effectively capturing both appearance and motion dynamics. However, these methods often have high computational demands, especially for tasks that require precise time-series modeling. Recently, with advancements in human pose estimation, skeleton-based approaches have gained popularity in human motion analysis due to their efficiency and effectiveness. Our research lies in the integration of skeleton-based human pose estimation with real-time video analysis for laboratory safety management. Unlike traditional methods, this approach reduces computational complexity while maintaining high accuracy in detecting unsafe behaviors. Moreover, the potential for our work to transform laboratory safety monitoring is evident, allowing for continuous and automated behavior detection without the need for constant human oversight. This system not only improves safety management but also helps prevent accidents by providing timely interventions. The research presented in this paper aligns with the journal's focus on computing and artificial intelligence. It demonstrates the application of deep learning techniques for real-time video surveillance and safety monitoring, contributing to the broader field of intelligent security systems. The organization of this paper is as follows.