# Enabling Technologies for Operator 4.0: A Survey
## 3 IoT-Based Solutions for Operator Activity Tracking  
From the viewpoint of operators, connection and conversion are the most critical levels of cyber-physical systems as these two levels are responsible for interaction. As smart sensors are key components of solutions for Cyber-Physical Production Systems (CPPS) [13], it is necessary to overview what kinds of tools are available for monitoring the activity of the operators.  
Usually, operator activity is monitored by Radio Frequency IDentification (RFID)-based object tracking [107]. This technology can collect real-time data about the activities of workers (operators) and machines, as well as movements of materials [108] and workpieces [109; 110]. Multi-agent supported RFID systems realize location-sensing systems [111] and intelligent-guided view systems [112]. RFID systems for human-activity monitoring provide an excellent opportunity to observe the work of the operators [113]. With the help of these devices, the whole production process, as well as production and waiting times have become measurable online. Based on this information, Shop Floor Control (SFC) and optimization can also be realized. When the RFID readers are placed such that the duration of the tasks can be estimated, how the production line is balanced in addition to the effect of product changes can be evaluated, as well as real-time data for OEE (Overall Equipment Effectiveness) calculations provided [114].  
The tracking of production can be significantly improved by the Indoor Positioning System (IPS) utilized for localizing the positions of the products and operators [95]. The applications of IPS and its potential benefits in terms of process development are complied in Table 4.  
Context-aware systems require unobtrusive sensors to track each step of the performed task [115]. As wearable sensors are becoming more common, their utilization is also becoming more attractive [116]. However, hand motion-based activity recognition is still challenging [117] and requires the application of advanced machine learning algorithms [118]. Tracking operator activity is a challenging and highly infrastructure-demanding task, which should utilize information stream fusion approaches to improve the robustness of the algorithms [119]. How all these smart sensor-based IoT technologies can be used to design Operator 4.0-type solutions is compiled in Table 5.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application Area** & **Description** & **Examples** \\ \hline Performance monitoring & Measure effects of process development and Business Process Reengineering (BPR). & Analyze moving- and staying-time of operators [120]. \\ \hline Movement analysis & Spaghetti diagram of operator movement to reduce unnecessary movement and optimize the layout and supply chain. & Reduce the duration of material handling [121]. Reduce the number of unnecessary movements of operators [120]. Support real-time Manufacturing Execution Systems (MES) [122]. \\ \hline Support 5S workplace organization methodology projects & Track tools and optimize the place of application and storage. & Decrease of stock and scrap. Improve activity times [120]. \\ \hline Digital twin & Direct process the on-line information inside the process-simulation tools. Prove the real-time architecture for the digital twin method. & The main elements of the real-time architecture are the ‘digital twin’ and IPS [123]. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Applications of indoor positioning systems in production management.