# A Scoping Review on Virtual Reality-Based Industrial Training
## 3 Results
### Literature Review
#### Industrial Training  
Training in the industry must be a continuous and updated, efficient process to obtain workers with the necessary skills to move a company forward. In this section, the works focused on industrial training without the intervention of VR are presented. Assistive systems made while using Head-Mounted displays (HMDs) are becoming important in huge virtualization domains, especially for the industrial training domain, because they improve the efficiency and quality of procedural tasks. However, most studies are limited because of inadequate task complexity, lacking comparisons, and measured variables. The research that was conducted by [PERSON] et al. [39] introduced a novel multimodal HMD-based training application and compared it to paper-based learning for manual assembly tasks. The research is performed in 30 participants, where it measures the training transfer of an engine assembly training task, user satisfaction, and perceived workload during the experiment. The participants perform faster while using the virtual system than when using paper-based instructions. [PERSON] et al. [40] develop a training system for archiving information through artificial intelligence in JAVA. The purpose of this research is focused on facilitating the printing of files and classifying each worker according to his rank in the company, so that his entry into the database can be efficient. As a result, surprisingly, it is emphasized that the proposed system operates perfectly, which is, without any error. A method that employs 3D simulations for new employee training, cost reduction, and induction process time is proposed by [41]. The selected processes for applying this methodology have been thoroughly evaluated, while taking the difficulty and precision they require into account. These areas include robotics, maintenance, and manufacturing. On the other hand, this new methodology consists of different stages, which allow the operator to organize the knowledge that he is acquiring and put it into practice according to the company's requirements. [PERSON] et al. [42] focus their research on developing a Toyota Production System (TPS) training course. This training was addressed to all production personnel, which represented a sample of 30 people. It is worth mentioning that the participants took a test about their knowledge of TPS before and after this training. Finally, this work obtained the results that, based on the carried out evaluations, the production staff improved their knowledge and skills regarding the Toyota production system by 37.5%. When speaking of industrial training, it is essential to mention the technological advances of recent years, the results of which have allowed the creation of increasingly efficient training models. In this context, [PERSON] et al. [43] proposed the use of augmented reality as a training tool to acquire skills in the field of industrial automation. The presented solution is based on an open-source platform, for which the cost of the system is considered to be affordable. The use of the A-Frame web tool, in combination with the JavaScriptproject called AR.js, allowed for developing interfaces to monitor, in real-time, the level of the AFB production line designed by Festo Didactics. As a final description of this section, the work that was carried out by [PERSON] et al. [44], who discussed the concept of e-learning in a training station, intended to teach new or novice workers the steps to follow to assemble a ski without the need for frequent human supervision. The training technique developed allows, based on the needs and behavior of the user, the delivery of the necessary information and instructions in order to complete their task. The training system consists of a work bench, which is necessary for mounting and supporting the skis, as well as racks that store the essential parts to complete the assembly. On the other hand, an RGB camera and two depth sensors are also used in order to capture and interpret the movements of the operator in the most precise way.