## 7 Conclusions  
In this paper, we explored the practicality of applying video analysis technology into abnormal behavior monitoring in laboratories. Our main contributions are as follows: (1) To ensure experimental safety, we collected video data related to the proper use of personal protective equipment (PPE), including lab coats, hats, gloves, and masks, and smoking behavior. (2) The dataset was analyzed using object detection (YOLO) and joint extraction (HRNet) algorithms for feature detection, followed by abnormal behavior recognition using the ST-GCN model. (3) To enhance computational speed, we developed an improved HRNet model and compared its performance against other models. We proposed to adapt object detection, joint extraction, and a graph convolution network for human action recognition and behavior classification, which addressed the evaluation of unsafe behaviors in laboratories and the limitations of current research. The experimental results show that the accuracy of abnormal behavior detection and recognition based on human key points reaches over 85%, which demonstrates that our method achieves high classification accuracy and effectively identifies abnormal behavior, offering new theoretical insights and practical support for laboratory safety management. To guarantee the effectiveness of our method, we compare different models including YOLOv3, YOLOv4, YOLOv5, and YOLOX for behavior classification; YOLOX performs best with 85.9% accuracy, 82.5% precision, 87.6% recall, 86.3% F1 score, and a relatively low number of parameters. Moreover, YOLOX exhibits good performance on different PPE classes, with a maximum accuracy of 93.7% on glove wearing. This paper introduces a real-time, automated abnormal behavior detection method based on video analysis, which streamlines the process of routine laboratory \"flying inspections\" and helps prevent temporary fixes made in response to surprise inspections. By enabling continuous monitoring of laboratory safety behavior, this method plays a crucial role in laboratory safety management and behavior warning systems in universities. It helps identify and mitigate safety hazards, prevent and reduce the occurrence of accidents, and ultimately improves the efficiency and effectiveness of laboratory safety management, ensuring the safe and orderly operation of laboratories. In future studies, we will focus more on a broader range of unsafe actions, and the method proposed in this paper can be expanded in future research to define additional abnormal behaviors based on actual needs, allowing for more comprehensive detection. Additionally, since this paper detects actions using a skeleton-based approach focused on human poses, it is important to consider that many abnormal actions involve interactions between individuals and their external environment. Future research could incorporate object detection and other technologies to analyze the environment and better identify interactions between people and their surroundings, thereby enhancing the accuracy of abnormal action detection. Conceptualization, Y.Y. and H.Z. ; methodology, Y.Y. ; software, Y.Y. and H.W. ; validation, Y.Y. and H.W. ; formal analysis, H.W. ; investigation, Y.Y. and H.W. ; resources, Y.Y. ; data curation, Y.Y. ; writing--original draft preparation, H.W. ; writing--review and editing, Y.Y. and H.Z. ; visualization, Y.Y. ; supervision, Y.Y. and H.Z. ; project administration, Y.Y. and H.Z. ;funding acquisition, Y.Y. and H.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key Research and Development Program of China, grant number 2022 YFC3602601 and Experimental Technology Reaseach Project of Zhejiang University, grant number SYBJS202314. Not applicable. The data presented in this study are available upon request from the corresponding author. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: