## 5 Implementation of the Study
### Performance Evaluation  
In evaluating the performance, assessing accuracy was conducted on the proposed model's object detection and human pose identification. Table 2 presents two experimental results for the object detection accuracy, precision, and recall (with 1000 and 12,000 datasets). When implementing 1000 datasets in real time, the model produced an approximate average accuracy of 96%. On the other hand, when implementing 12,000 data, the average accuracy was approximately 97%. The results suggest that employing both datasets provides insignificantly different results. Additionally, Figure 8 depicts a confusion matrix of six objects: worker, machine, control box, toolbox, tool, and product. Figure 5: Examples of worker pose classification conducted per frame: (**a**) standing, (**b**) walking, (**c**) holding, (**d**) touching, (**e**) bending, (**f**) squatting, and (**g**) sitting. ```
Input: Frame ((f(t)), Object_label, Bbox, Pose_label Output: Descriptive text of worker behavior (normal/anomalous behavior)
1. Generate Human-Object {
2. f(t), object_label, bbox, pose_label  Associate each pose_label, object_label, and Bbox per frame f(t) (distance threshold)
3. Perform action recognition: object_label[n], pose_label[n], distance[i][j], sentence_list
4. return human_object_interaction }
5. Generate descriptive_text {
6. F(t), human_object_interaction, list_behavior
7. Compare the interaction (human_object_interaction, list_behavior) per frame (f[(t - 1), (t + 1)])
8. Analyze the context and flag_classification (normal/anomalous). 9. if flag_classification = True:
10. Descriptive_text1 \(\leftarrow\) \"Anomalous Behavior\"
11. else:
12. Descriptive_text1 \(\leftarrow\) \"Normal Behavior\"
13. return Text (Descriptive text of worker behavior, normal/anomalous )
```  
**Algorithm 1**Worker behavior description  
Table 3 details the model's performance on human poses in terms of accuracy, precision, and recall from 21 to 31 videos. In this case, the video contains a total of 39,005 frames and 37 anomalous events: sitting, bending in front of the machine, and when the worker is not seen in the frame. For training and testing, when implementing 21 videos, the model achieved satisfying accuracy rates of 94% and 93%, respectively. Moreover, when implementing 31 videos, the model achieved a similarly satisfying accuracy of approximately 95% and 94%, respectively; in addition, no overfitting was found. Furthermore, the model successfully performed the highest accuracy for standing, touching, and holding poses (approximately 95% for 21 videos and 96% for 31 videos). Out of the overall poses, when implementing 21 and 31 videos, the sitting pose performed the lowest accuracy of, approximately 88% and 90%, respectively. For a clearer description, the confusion matrix of the seven poses is as follows: standing, walking, touching, holding, bending, sitting, and squatting (illustrated in Figure 9). Figure 6: Behavior classification and recognition toward the relation between workers and objects in creating descriptive texts. Figure 7: Examples of worker behavior and description (normal/anomalous) results within frames. Normal behavior: (**a**) the result in the form of a video frame, (**b**) a worker is standing in front of a machine, (**c**) a worker is walking, (**d**) a worker is touching the control box, and (**e**) a worker is holding a tool. Anomalous behavior: (**f**) a worker is bending in front of a machine and (**g**) a worker is sitting in front of a machine.