## 6 Conclusions  
To resolve problems in manufacturing industries, this study provides a model that detects anomalous behavior in manufacturing environments. The focus is to classify anomalous worker behavior to subsequently label the behavior by utilizing descriptive texts. To provide the expected results, relevant scenarios have been conducted that encompass recognizing the behavior from object detection and human pose based on a threshold. Afterward, classifying the anomalies related to human pose recognition (emergency: worker falls, slippage, or illness) and anomalies related to the recognition of human poses with object positions (tool breakage and machine failure). Next, the worker behavior within the manufacturing environment was classified as normal or anomalous behavior using a worker behavior description algorithm. In this case, the proposed model successfully detected anomalous behavior within the tested videos consisting of anomalous worker behavior and unexpected worker interaction with surrounding objects, while effectively preserving the target with an acceptable object detection accuracy of approximately 97% and the highest pose recognition accuracy of approximately 96% (for standing, touching, and holding); the lowest accuracy was approximately 88% (for the sitting pose). The results did not exhibit significant differences, and the gap was no more than one percent. In fact, some poses were incorrectly detected. For a more detailed example, in one case, the model could not properly align the positions of the overall body landmarks, thus leading to landmark misalignment. In a different case, the hand landmarks were not identified as covered by the body landmarks.  
Therefore, a betterment of the current work is required in future studies by expanding requirement scenarios on abnormal real-time localization in terms of target tracking from multiple-target anomalous behavior to detect anomalies. In this particular context, the studies are expected to enhance the scope from individual workers to interactions between multiple workers and objects. The goal is to provide a more comprehensive understanding of the manufacturing environment by adding larger and more diverse datasets to improve the performance. In addition, implementing and deploying the model in real-time monitoring systems are required to create practical and useful models in real-world manufacturing environments. These practices involve optimizing the model's computational efficiency, ensuring low latency, and developing user-friendly interfaces for workers and supervisors to interpret and respond to anomalous alerts effectively. Moreover, advanced Mask R-CNN versions are feasibly pre-trained to enhance object detection.  
Conceptualization, R.R., M.H. and K.J.; methodology, R.R., M.H. and K.J.; software, R.R.; validation, R.R., M.H. and K.J.; formal analysis, R.R., M.H. and K.J.; investigation, R.R., M.H. and K.J.; resources, K.J.; data curation, R.R.; writing--original draft preparation, R.R., M.H. and K.J.; writing--review and editing, R.R. and K.J.; visualization, R.R.; supervision, K.J.; project administration, K.J.; funding acquisition, K.J. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Examples of output snapshots with incorrect classification: (**a**) incorrect alignment of positions of overall body landmarks and (**b**) failure to identify hand landmarks as being covered by body landmarks.  
**Funding:** This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2016-0-00318), supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available from authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.