# Towards an Assembly Support System with Dynamic Bayesian Network
## 2 Related Work  
Cyber-physical systems produce major changes in entire industries, especially in the factory area, where there is an ever-increasing discussion about the role of operators in the production processes. In [13], the authors identified an increased demand in highly customizable smart products. Due to the overhead costs, the use of a human workforce is preferred. They are developing a scalable assistance module for the assembly process with a big focus on the ergonomics of the workplace. In [14], the authors wrote about how the assistance systems increase productivity in assembly processes, and they formulated the principles of designing assembly assistance systems. They stated that it is unclear what problems might appear in the representation of information in manual assemblies or how these problems should be solved using assistive systems. By using two example cases, the authors could identify such problems. The authors split the identified problems into five categories of information representation problems: scarcity of the input provided to the work system, irrelevancy of data, outdated information, and lack of process orientation, incompatibility of information representation with human interpretation. From all these problems, we partially encountered data irrelevancy. We addressed this problem by eliminating irrelevant data. The authors of [15] state that the manufacturing trends are heading towards high-variant and small quantity assembly of products. Companies started using cognitive assistance systems with the aim of increasing the quality and efficiency while decreasing the cognitive load of the workers. They state that the workers' lack of acceptance towards assistive systems decreases their benefits. An approach for an assembly workstation with multiple software and hardware components was proposed. Similarly, our system has various software and hardware components, the main differences being the presence of the next assembly step suggestion software component and the usage of a large touchscreen instead of a projector for visual communication with the worker. In [16], the authors analyzed the technology that an assistive system should encompass. They started by analyzing the existing assembly assistance systems and how they can optimize those systems. Experts were interviewed to determine what functionalitiesan assembly assistance system should have. Several methods were proposed for implementation, such as intelligent image processing, deep learning algorithms, gamification, or augmented reality. In our work, due to the modular nature of our customizable tablet and a high number of assembling possibilities, gamification represents a core principle. Assembly support systems can be found in the literature with different implementation complexities and enabled by different technologies. The simplest ones allow the operators to visualize the instructions. The operators move to the next step when they consider that the current step is finished and carried out correctly. Examples include traditional assembly manuals or new approaches that use AR technologies [17; 18] that superimpose the information over the real object. Advanced assembly support systems can also extract information about the current step in the assembly process. This is achieved by recognizing the current user action, fusing information from different sensors, such as electromyography sensors, inertial measurement unit, and depth RGB camera [19]. Another way is to recognize the current assembly state of the product using a simple RGB camera, as in [20], for example. More complex assembly support systems use the information about the current state of the system to predict future human actions [20; 21; 22]. The use case from [20] predicts the next assembly state to detect faults and mistakes in a predefined and ordered assembly process. This is achieved using an end-to-end neural network that takes as input only images of the assembled product without any other information about the operator.