The way organs are positioned and moved in the workplace can cause pain and physical harm. Therefore, ergonomists use ergonomic risk assessments based on visual observation of the workplace, or review pictures and videos taken in the workplace. Sometimes the workers in the photos are not in perfect condition. Some parts of the workers' bodies may not be in the camera's field of view, could be obscured by objects, or by self-occlusion, this is the main problem in 2D human posture recognition. It is difficult to predict the position of body parts when they are not visible in the image, and geometric mathematical methods are not entirely suitable for this purpose. Therefore, we created a dataset with artificial images of a 3D human model, specifically for painful postures, and real human photos from different viewpoints. Each image we captured was based on a predefined joint angle for each 3D model or human model. We created various images, including images where some body parts are not visible. Nevertheless, the joint angle is estimated beforehand, so we could study the case by converting the input images into the sequence of joint connections between predefined body parts and extracting the desired joint angle with a convolutional neural network. In the end, we obtained root mean square error (RMSE) of 12.89 and mean absolute error (MAE) of 4.7 on the test dataset.

翻译：工作场所中器官的定位与运动方式可能导致疼痛和身体损伤。因此，人机工程学专家通常通过视觉观察工作场所，或审查工作场所拍摄的照片与视频来进行人机工程学风险评估。然而，照片中的工作者姿态往往并非处于理想状态：其身体某些部位可能不在摄像机视野内、被物体遮挡或发生自遮挡——这正是二维人体姿态识别中的核心难题。当身体部位在图像中不可见时，难以预测其位置，而几何数学方法对此并不完全适用。为此，我们构建了一个包含三维人体模型人工图像（特别针对疼痛姿态）以及多视角真实人体照片的数据集。每张图像的采集均基于预先设定的三维模型或人体模型关节角度。我们生成了多种图像，包括部分身体部位不可见的图像。尽管如此，由于关节角度已预先标定，我们可通过将输入图像转换为预定义身体部位间的关节连接序列，并利用卷积神经网络提取目标关节角度进行研究。最终在测试集上获得了12.89的均方根误差（RMSE）和4.7的平均绝对误差（MAE）。