## 4 Features and Issues of Existing Applications
### Analysis and Feedback  
Human-focused DT applications for OSH should support data analysis and feedback helpful for the prevention of occupational injuries and illness. Figure 11 summarizes the sorts of feedback provided by existing applications. An 'assessment result' is obtained following an analysis or calculation using the data collected from a workplace, while a'measurement result' indicates the collected data. 'Robot control' refers to the associated commands or signals generated by a DT application to control industrial robots if required. 'Real-time alert and guide' is a type of feedback containing information about risks and hazards identified in the workplace, and requesting human workers or managers to take proper actions. 'Assessment result' feedback is often provided in the form of a report that includes ergonomic or safety indices [8; 14; 17; 24; 25; 27; 30; 31; 32; 33; 34; 40; 41; 42], the stability of a human's motions [15], and joint torque [26; 33]. This feedback can be used to redesign manual tasks and workspaces, and it is frequently provided off-line. The 'robot control' type of feedback aims to control industrial robots more ergonomically and safely. For instance, [PERSON] et al. [29], [PERSON] et al. [34], [PERSON] and [PERSON] [37], and [PERSON] et al. [4] developed DT applications that stop the robot or reduce its speed when the safety distance between a human worker and a robot becomes unsafe. [PERSON] et al. [28], [PERSON] et al. [34], [PERSON] et al. [39], [PERSON] and [PERSON] [37], and [PERSON] et al. [38] applied DT applications to make robots perform necessary actions, such as delivering the right tools and moving through the right paths at the right time. Those applications generally took both productivity and OSH issues into consideration. In addition, [PERSON] et al. [35] studied DT applications that lead human workers to maintain correct posture. Figure 11: Feedback provided by the existing applications. The'real-time alert and guide' type of feedback contains a wide range of information about OSH issues, including body segments that cause bad posture [24; 31], guides for work [39], and the occurrence of dangerous situations [13; 36]. 'Measurement result' feedback includes the location of human workers [13; 36] and their biometric information [40]. In Figure 12, important data analysis methods and approaches for human-focused DT applications for OSH are classified into four groups: observational methods, AI/machine learning (ML)/data mining (DM), simulation, and safety distance. Typically, the objective of observational methods such as RULA, REBA, and OWAS is to assess the risks in manual work by calculating ergonomic or safety indices. To apply observational methods, data about the posture of a human worker and any manual work must be collected and provided. Traditionally, the data for observational methods have been collected by human experts, who observe the manual work performed by a human worker. However, DT can be used to collect data in an automated and real-time manner, and most existing applications can also be used as the data collection tool for observational methods. Recently, AI/ML/DM algorithms, which can be used to discover useful patterns and knowledge hidden in a large volume of data, have emerged as promising data analysis techniques in a wide range of application domains. Specifically, artificial neural network (ANN) and deep learning (DL) algorithms have been successfully applied to analyze unstructured data such as those from images and videos, which are typically hard to analyze using traditional algorithms. Some existing applications adopted these algorithms to perform object detection [4; 35; 36] and human pose estimation [31; 37; 39]. Object detection and human pose estimation are integral parts of human-focused DT applications if data about a human worker are collected in the form of images or videos. Additionally, AI/ML/DM algorithms have been used for human motion trajectory prediction [28; 34], task scheduling [38], etc. [13; 15].