**3. Methods**

#### *3.1. Research Framework*

Feature engineering needs to set the transcendental extracted features artificially. Deep learning training is a process of finding the optimal solution based on a dataset using representation. Unlike feature engineering, representation learning is a group of machine learning methods that can automatically find useful input data features through a generalpurpose learning procedure [55–57]. The representation learning is similar to a "black box", and therefore, it is difficult to understand how the internal nonlinear function works. As the dataset and the algorithm are the critical factors in applied research related to deep learning, the research process is designed by improving design science research [58]. In this study, this is more suitable for research on the automatic detection of workers' unsafe actions, as shown in Figure 2.

**Figure 2.** The research process of intelligent recognition for workers' unsafe action.

The method is structured as follows:


Therefore, the test results demonstrate the model's performance and are the basis of the design and plan modifications.
