## 1 Introduction
They claim that with increasing complexity and variability of products, the main difficulty for the operator is to follow the right assembly procedure to manufacture the desired product correctly. To support the procedure, there are many possible alternatives for setting up a smart working environment (i.e., pick/put-by-light/voice/vision systems) to guide the human operator during the assembly process. There are also alternative recommendations, like augmented reality (AR), virtual reality (VR) instructions [10; 13; 27; 28] and projector-based digital instructions [27]. In smart assembly processes there are more studies reporting the use of AR application to assist workers through assembly process in comparison to VR applications. One of the possible reason is described in [PERSON] et al. [29]. Authors explained that augmented reality technologies allow the user to see the real environment with virtual objects superimposed upon the real world. AR is potentially cheaper and more realistic tool for assembly design than VR. AR systems can utilize established techniques in VR and evaluate the assembly more realistically with a combination of virtual and real objects in the actual workplace. [PERSON] et al. [10] propose a smart instruction system with the support of AR and deep learning-based tool detection, which is intended to improve worker performance through assistive instructions. To evaluate the integrated AR system, they compared the paper manual provided by the manufacturer of the computer numerical control (CNC) carving machine and the AR instruction. The result of usage of smart instruction system showed reduction of completion time and the number of assembly errors compared to the paper manual. [PERSON] [28] emphasizes AR as the information-level real-time visualization method (IRV), which transforms real-time constraints into computer-generated real-time graphics, called information level real-time AR instruction (IRAI). In a case study, they analyzed and compared the performance of participants under real-time AR instructions with different information levels. The research shows that a high cognitive level of IRAI can make individuals have better performance in work efficiency and cognitive efficiency. To my best knowledge, this is the first user study to evaluate the results of real-time visual information design, so there are many potential research areas in the future. The evaluation of AR visualization assistive systems in reported papers showed common results that untrained users can assemble products faster and with a lower error rate, which is the goal in the industry and the proposed research. Therefore, our proposal and the aims of the research are to: (i) develop a smart algorithm that controls newly designed smart workstation with smart technologies and digital instructions for assembly tasks, which is flexible and fully adjustable and eliminates the limitations of the traditional workstation, (ii) evaluate the functionalities of the smart workstation with the performance of the operator, such as time and number of errors compared to the traditional workstation, and (iii) ergonomically evaluate reaching motion with the DHM approach. The advantage of the proposed system is the self-configuration of the entire assembly workstation for the needs of an individual worker, according to gender and body height (constitution), to ensure the ergonomic performance of the assembly process. Moreover, the usage of the system is simple enough, so that thorough training in technology and operation is not necessary. This means that the assembly process can be carried out by an untrained worker without delays and errors. From the literature it can be understood that the need for human labor is great and that only with a properly designed work environment it is possible to perform jobs efficiently to consequently increase productivity in the industry.