## 5 Conclusions and Further Remarks  
### Conclusions  
DT is one of the most important Industry 4.0 technologies, and is recognized as a key component of a smart factory. However, many DT applications only consider machines or materials as physical assets, and not human workers, due to several obstacles such as the complexity of human behaviors, the technical difficulties of collecting digital data from human workers, and psychological resistance from human workers. However, human workers are the main subject of OSH. Thus, human workers are an important physical asset for DT applications for OSH, and modern Industry 4.0 technologies enable the collection of much digital data from human workers. In this context, the authors believe that human-focused DT application for OSH is a promising research topic for both researchers and practitioners. This paper provides a brief survey on DT applications that concern OSH within workplaces. Specifically, this paper regards visual representation for the human worker as an integral part of human-focused DT applications for OSH. From the survey results, the following conclusions can be drawn. First, only 25 relevant publications were identified, which indicates that human-focused DT for OSH is a very specific application domain of DT technology. Moreover, Europe accounts for more than half of the relevant publications. In other words, human-focused DT is not yet a popular research topic in some regions. OSH management generally does not yield immediate financial benefit, and specialized hardware and software modules are required to collect digital data from human workers and to visualize them in digital environments. Nevertheless, human workers are still an essential part of smart factories in the Industry 4.0 era, and the number of relevant publications is increasing. Second, computer science and industrial engineering are the most frequent research areas for journals that publish relevant research papers. In other words, computer science and industrial engineering provide important methodological and theoretical bases for human-focused DT for OSH. This is partly due to the fact that a significant number of existing applications aim to automate data collection and analysis procedures for traditional observational methods. These observational methods, such as RULA, REBA, and OWAS, are generally useful for assessing postural risks by calculating ergonomic or safety indices. Traditionally, the data required to calculate indices are collected and analyzed manually by industrial engineers, which is inconvenient. Modern Industry 4.0 technologies such as sensor networks and MoCap systems can, however, be used to fill this gap. Thus, computer science is also an important research area for human-focused DT for OSH. Existing applications for automating observational methods have been innovative; however, they do not consider new types of OSH issues, caused by modern manufacturing facilities within smart factory environments. Specifically, DT technology is expected to lead to the creation of novel approaches and applications for preventing risks and hazards caused by robots that can be actively introduced into many workplaces. Thus, robotics is also an important research area for human-focused DT for OSH. Third, the most popular application domain is assembly tasks in the manufacturing industry. Typically, an assembly task contains a sequence of actions. DT can be used not only to assess postural risks of specific actions, but also to check the progress of the assembly and to provide dynamic guidance and support to human workers. In other words, human-focused DT applications can contribute to both productivity and OSH, and they might be prevalent in future smart factory environments. Fourth, cameras, including the Kinect system, are becoming popular as data collection devices. In addition, posture data, represented by the positions of joints and joint angles, are the most popular data type for human-focused DT for OSH.