## 5 Discussion  
### Discussion on Experimental Results  
The QCES system performs human skeleton recognition on all the working posture images collected in the experiment, generating human skeleton diagrams as shown in Figure 11. The generated human skeleton diagrams show no obvious defects or limb misalignments, indicating that the QCES recognition effect is good, and the coordinate data of the identified key points can be used for subsequent analysis. The skeletal points of the working posture recorded by the OptiTrack motion capture system are depicted in Figure 12a, and the joint angles created in the seeker software from these three skeletal points are shown in Figure 12b. The overall recorded posture data is complete, with no missing key points, and the coordinate data of the identified key points can be used for subsequent analysis. The previous sections completed the quantitative statistical identification and analysis of the verification experiment, validating the reliability and feasibility of the QCES system. The study demonstrated that the QCES system has good consistency with the motion capture system in recognizing limb angle data. It also maintains strong consistency with expert assessments in REBA and RULA evaluation scores. However, some detailed data in the experimental results show differences, requiring further analysis and discussion. Figure 10: (**a**) The total REBA assessment score for males and females. (**b**) The total RULA assessment score for males and females. In the angle analysis, the average Spearman correlation coefficient for 18 postures is 0.87, slightly higher than the coefficient mentioned in [PERSON] et al. 's study [45] as shown in Table 3. In the REBA and RULA assessments, 81.45% of the QCES system's evaluation results are entirely consistent with the expert assessment results. The Cohen's kappa coefficient's average value in the six evaluation tables is 0.766, and the average Root Mean Square Error (RSME) is 0.408. Both are higher than the results of [PERSON] et al. 's study [45] (average [PERSON]'s kappa coefficient of 0.624), [PERSON] et al. [33] (average [PERSON]'s kappa coefficient of 0.71, average RSME of 0.41), and [PERSON] et al. [45] (average [PERSON]'s kappa coefficient of 0.714, average RSME of 0.646). This demonstrates that the QCES system currently exhibits higher accuracy and stability in limb angle recognition and RULA and REBA assessment results, outperforming similar available products. However, as shown in Table 3, the RMSE values for the two arms in limb angle are relatively high, with the RMSE values for the left upper arm, left forearm, right upper arm,  
Figure 11: In this experiment, all subjects QCES identified the human skeleton map generated. Figure 12: (**a**) The human skeleton points identified by OptiTrack motion capture device in this experiment (**b**) The joint angles generated by seeker software. and right forearm being 7.81, 5.94, 5.30, and 7.78, respectively. This could be due to the fact that when the subjects are removing garbage bags, the bags may obstruct the arms to varying degrees, affecting the recognition performance. It is recommended to handle the objects held in hand during the evaluation calculation to avoid obstruction. The study found that the QCES system and expert assessment results showed slight differences in the REBA scores for postures W3-45, M2-60, and M2-90, as well as in the RULA score for posture M2-60. However, for all other postures, the QCES system's assessment results were entirely consistent with the expert assessments. Upon retracing the original data, it was discovered that in the REBA assessment of posture W3-45, the expert assessment score for score A was 5 points, while the QCES system assessment score was 4 points. This discrepancy was due to a deviation in the judgment of the forward tilt angle of the neck. The REBA assessment rules state that a neck twist exceeding 20\({}^{\circ}\) is scored as +4 points, while a twist within 20\({}^{\circ}\) is scored as +2 points.