## 5 Discussion
### Research Contribution and Limitations  
The theoretical and practical contributions of this research are as follows:  
1. This study constructs a human body skeletal joint model for analyzing workers' working postures, defines limb angle calculation formulas and limb state judgment procedures, and discusses the feasibility of musculoskeletal disorder assessment from work posture analysis, enriching the fundamental theoretical research of musculoskeletal disorder risk assessment. 2. This study combines artificial intelligence algorithms such as convolutional neural networks, pose machines, and convolutional pose machines to apply body skeletal recognition algorithms to musculoskeletal disorder risk assessment, enriching the application of image recognition and other artificial intelligence algorithms in the field of human factors engineering. The research also develops calculation modules for the REBA and RULA assessment methods, improving the accuracy of the system's assessment and providing new ideas for the development of subsequent assessment methods. 3. This study conducts comparative validation experiments of the QCES system with OptiTrack motion capture and human factors engineering experts, verifying the reliability of the body skeletal recognition algorithm in the application of musculoskeletal disorder risk assessment and the feasibility of automated assessment procedures for musculoskeletal disorder risk assessment. The experimental design and data analysis methods and results in this study can provide new insights and references for subsequent research on musculoskeletal disorder risk assessment and system validation experiments in the field of human factors engineering. From an academic paper perspective, the engineering contributions of this study are as follows:  
1. The proposed system in this study utilizes smartphones as carriers, addressing the cumbersome and time-consuming nature of musculoskeletal disorder assessments. This innovation enables the widespread application of intelligent assessment systems for evaluating factory workers' working postures. For example, as mentioned in this paper, city management personnel can use the assessment results of the QCES system to improve the tilt angle of waste containers in decision-making, thereby minimizing the risk of WMSDs for sanitation workers when handling garbage bags and reducing the consumption of human and material resources. 2. In experimental validation, we found that the QCES system exhibits high assessment efficiency and accuracy. The assessment reports provide detailed evaluation data for multiple assessment methods, offering a powerful assessment tool for researchers in related fields to conduct in-depth investigations and analyses. 3. In the study of sanitation workers, this research found that adjusting the tilt angle of waste containers, without altering their original design, can improve the work ing posture of sanitation workers when removing garbage and reduce the risk of musculoskeletal disorders associated with such tasks. There are still some limitations that need to be addressed, including:  
1. The current system only completed comprehensive assessments of the REBA and RULA assessment methods, providing detailed data for the assessments conducted by these two methods. However, each assessment method has its own strengths and limitations in practical work. Therefore, it is necessary to further expand the musculoskeletal disorder risk assessment methods based on the proposed system to meet the needs of different working environments for musculoskeletal disorder risk assessment methods. 2. The experimental design included the assessment of WMSDs risks for sanitation workers when clearing waste containers and the comparative assessment of the improved solution, forming a relatively complete process of assessing-dangerous postures-improvement-results comparison. However, this study only evaluated and tested one occupation.