## 4 Proposed Method  
Figure 1 illustrates the architecture of the proposed method to provide an overview of the developed model. The model consists of object detection, pose identification, pose classification, and behavior classification description stages. The purpose is to identify worker anomalous behavior in a manufacturing environment. The output is descriptive text generated based on the identified worker pose and interaction with surrounding objects. The stages are detailed as follows. First, the input feature (video frames) is extracted. In this case, sequential frames are extracted from the video from the top of the convolutional layer to identify the transformation of each movement in each frame. The entire dataset is divided into training and testing data in 75% and 25% ratios. The model classifies objects using an input image size of 224 \(\times\) 224 \(\times\) 3 (RGB). The next step is object detection, where each object in the sequential video frames is detected using Mask R-CNN methodology by producing a fixed size from each feature map. Afterward, a completely connected layer with a similar input size is created to enable convolutional and deconvolution neural networks to detect object classes (labels). Subsequently, it creates bounding boxes (bbox) and segmentation masks. The underlying reason for utilizing R-CNN Mask is its capability to detect each small object and overlapping objects in a more specific and efficient way compared to other models such as Fast R-CNN, Faster R-CNN, Single-Shot MultiBox Detector (YOLO), and Single-Shot MultiBox Detector (SSD). Additionally, Mask R-CNN is suitable for the requirements and focus of the study. Figure 1: Proposed architecture. In the next stage, poses are identified and classified to recognize worker body movement. Within the process, the model acquires pose landmarks from the MediaPipe Holistic framework, and subsequently examines the overall body parts to determine the precise locations of human body key points. The purpose is to understand the articulation of an individual's joints and body parts. As a matter of fact, this stage is intended to generate a label for each part of the human body prior to extracting and classifying the workers' poses, such as standing, walking, touching, holding, bending, sitting, and squatting. Following the forementioned pose classification stage, the model accordingly describes behavior classification (determines possible anomalous behavior) and generates descriptive text based on the identified worker pose and interaction with surrounding objects. In this study, the behavior is identified as normal or anomalous subsequent to adjusting to the real conditions within manufacturing environment. To illustrate this, the sitting pose, in general, is considered a neutral pose; however, in case the sitting pose is performed on an object, a combination of the pose and the object is considered as a behavior and is classified as normal or abnormal based on the location or domain. For a more comprehensive illustration, a student is sitting on a chair in a classroom, for example. This behavior is classified as normal, as sitting in the classroom is normal. However, when the behavior is performed in a manufacturing environment, the behavior is classified as anomalous, as the behavior is forbidden according to the working procedure. For a more detailed example, a worker is sitting in front of a machine located in a manufacturing environment. This is classified as anomalous behavior as it is considered to deviate from the working procedure. In addition, Figure 2 displays anomalous conditions derived from video frames. Figure 2a-c presents normal conditions: a worker is precisely standing in front of a machine (a), a worker is touching a control box in front of a machine (b), and a worker is holding a tool in front of a machine (c).