## 1 Introduction
### Examples of Technologies to Support Ergonomic Assessments  
With the development of artificial intelligence technology, [PERSON] et al. have proposed the integration of computer image recognition technology with self-reports, questionnaires, and observational methods to improve assessment efficiency and accuracy ([PERSON] et al., 2018). However, different implementation methods of technological assessment have fundamental differences in the human skeleton recognition module and are typically divided into two categories: one involves hardware-assisted recognition, and the other relies solely on algorithms, such as convolutional neural networks and convolutional pose machines. Currently, in research utilizing hardware-assisted recognition, Kinect depth cameras are widely used, capable of recognizing the three-dimensional positions of over twenty human body joints ([PERSON], 2018). The accompanying software uses decision trees and random forest classification algorithms to classify and identify human skeletal joints. However, the studies by [PERSON] et al. (2019) and [PERSON] et al. (2019) have both indicated that when Kinect is used for the on-site assessment of workers, it exhibits significant instability due to on-site environmental factors, object occlusion, and lighting, leading to a substantial decrease in accuracy. In the purely algorithmic domain, significant progress has been made in the field of image processing with the advent of deep learning technology, particularly Convolutional Neural Networks (CNN) ([PERSON] et al., 2019). Meanwhile, in the human skeletal recognition algorithm, [PERSON] et al. demonstrated in the field of autonomous driving that based on a Convolutional Pose Machine (CPM), the rapid recognition and prediction of traffic police gestures can be achieved ([PERSON] et al., 2019). [PERSON] et al. predicted human body joints from a single image and used angle data for MSD risk assessment, achieving a prediction accuracy of 89% and a kappa index of 0.71 ([PERSON] et al., 2019). Therefore, we believe that the combined application of Convolutional Neural Networks (CNN) and CPMs is a means of the real-time recognition of work postures. Although some open-source software, such as Mediapipe and YOLOv7 pose capture frameworks, can generate real-time detection on CPUs, they lack neck capture. [PERSON] et al. mentioned in their research the use of inverse kinematics to calculate the neck and waist joints to supplement Mediapipe and used Unity 3D for result analysis ([PERSON] et al., 2019). However, this approach has high computer hardware requirements and has a certain installation and learning cost.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Evaluation Process** & **Main Assessment Part** \\ \hline REBA ([PERSON], 2018) & The scores for each part of the body are determined based on the posture angle, and the risk level is adjusted based on torsion and other conditions. & Waist, neck, upper arm forearm, leg, wrist, frequency, grip, load \\ \hline RULA ([PERSON], 2018) & According to the angle of the posture of different parts of the body, it is classified and scored, and the risk level of WMSDs is obtained after correction. & Waist, neck, upper arm forearm, wrist, frequency, grip, load \\ \hline OWAS ([PERSON], 2018) & The posture form of each part of the body is coded, and the risk level is distinguished by the coding sequence. & Waist, neck, arm, leg, load \\ \hline NIOSE ([PERSON], 2018) & Many factors in the manual handling process are measured and collected, and the lifting risk is calculated. & Handling distance, height, speed, etc. \\ \hline RSI ([PERSON], 2018) & Measure or estimate five task variables in the work task, and the RSI value is a five-variable model using continuous multipliers. & Intensity of exertion (force), exertions per minute (frequency), duration per exertion, hand/wrist posture, duration of task per day \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common observation and assessment methods.