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.

通过创建一个数据集，包含了人体模型的人工图像和真实人体照片，以及通过卷积神经网络提取所需关节角度，我们得到了在测试数据集上的均方根误差（RMSE）为 12.89、平均绝对误差（MAE）为 4.7 的结果，从而解决了二维人体姿势识别中不可见身体部位的问题。