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The merger of game-based approaches and Virtual Reality (VR) environments that can enhance learning and training methodologies have a very promising future, reinforced by the widespread market-availability of affordable software and hardware tools for VR-environments. Rather than passive observers, users engage in those learning environments as active participants, permitting the development of exploration-based learning paradigms. There are separate reviews of VR technologies and serious games for educational and training purposes with a focus on only one knowledge area. However, this review covers 135 proposals for serious games in immersive VR-environments that are combinations of both VR and serious games and that offer end-user validation. First, an analysis of the forum, nationality, and date of publication of the articles is conducted. Then, the application domains, the target audience, the design of the game and its technological implementation, the performance evaluation procedure, and the results are analyzed. The aim here is to identify the factual standards of the proposed solutions and the differences between training and learning applications. Finally, the study lays the basis for future research lines that will develop serious games in immersive VR-environments, providing recommendations for the improvement of these tools and their successful application for the enhancement of both learning and training tasks.

Two teaching methodologies are presented and compared in this study: on the one hand, semi-guided tours in immersive virtual reality and, on the other, viewing video renderings of 3D environments. The two techniques are contrasted through 3D modeling of a fifteenth-century Spanish town called Briviesca, in an immersive environment, viewed with Oculus Rift. The suitability of virtual reality for teaching is assessed through questions on historical knowledge and urban layout. The understanding of the undergraduate students is evaluated, through questionnaires, after the viewing sessions. The responses of the students underline the effectiveness of the two methodologies: Video screenings received higher scores for historical ideas and the virtual tour was the most effective method at conveying knowledge learnt while viewing. Additionally, two user movements for controlling the virtual reality environment were tested: (1) gamepad locomotion and (2) roomscale movements combined with teleporting. The clear advantage of the second option was the total lack of motion sickness effects. However, the natural tendency using teleporting was to move very quickly through the city areas with no singular buildings and to spend more time in front of these types of buildings. They therefore missed visual information related to the first areas while retaining more information related to those buildings. Finally, the spatial location of singular buildings was clearly better acquired with the virtual tour.

The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining in factories. In this research the effect of a new strategy to improve machine-learning model accuracy is studied: experimental repetition. Tool-life prediction in the face-turning operations of AISI 1045 steel discs, depending on different cooling systems and tool geometries, is selected as a case study. Both the side rake and the relief angles of HSS tools are optimized using the Brandsma facing test under dry, MQL, and flooding conditions. Different machine-learning algorithms, such as regression trees, kNNs, artificial neural networks, and ensembles (bagging and Random Forest) are tested. On the one hand, the results of the study showed that artificial neural networks of Radial Basis Functions presented the highest model accuracy (11.4 mm RMSE), but required a very sensitive and complex tuning process. On the other hand, they demonstrated that ensembles, especially Random Forest, provided models with accuracy in the same range, but with no tuning procedure (12.8 mm RMSE). Secondly, the effect of an increased dataset size, by means of experimental repetition, is evaluated and compared with traditional experimental modelling that used average values. The results showed that some machine-learning techniques, including both ensemble types, significantly improved their accuracy with this strategy, by up to 23%. The results therefore suggested that the use of raw experimental data, rather than their averaged values, can achieve machine-learning models of higher accuracy for tool-wear processes.

Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.

Machine learning algorithms for classification are employed in this study to generate different models that can predict the surface roughness of parts manufactured from polyvinyl butyral by means of Fused Deposition Modeling (FDM). Five input variables are defined (layer height, print speed, number of perimeters, wall angle, and extruder temperature), and 16 parts are 3D printed, each with three different surfaces (48 surfaces in total). The print values used to print each part were defined by a fractionated orthogonal experimental design. Using a perthometer, the average value of surface roughness, Ra , on each surface was obtained. From these experimental values, 40 models were trained and validated. The model with the best prediction results was the one generated by bagging and Multilayer Perceptron (BMLP), with a Kappa statistic of 0.9143. The input variables with the highest influence on the surface finish are the wall angle and the layer height.

The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δfl). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation—with proper consideration to the amount of wear of cutting tool insert’s edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models.

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

Industry 4 [...]

Depression and anxiety in children and adolescents are major health problems worldwide. In recent years, serious games research has advanced in the development of tools to address these mental health conditions. However, there has not been an extensive analysis of these games, their tendencies, and capacities. This review aims to gather the most current serious games, published from 2015 to 2020, with a new approach focusing on their applications: awareness, prevention, detection, and therapy. The purpose is also to analyze the implementation, development, and evaluation of these tools to obtain trends, strengths, and weaknesses for future research lines. The identification of the serious games through a literature search was conducted on the databases PubMed, Scopus, Wiley, Taylor and Francis, Springer, PsycINFO, PsycArticles, Web of Science, and Science Direct. The identified records were screened to include only the manuscripts meeting these criteria: a serious game for PC, smartphone, or virtual reality; developed by research teams; targeting only depression or anxiety or both; aiming specifically at children or adolescents. A total of 34 studies have been found that developed serious games for PC, smartphone, and virtual reality devices and tested them in children and adolescents. Most of the games address both conditions and are applied in prevention and therapy. Nevertheless, there is a trend that anxiety is targeted more in childhood and depression targeted more in adolescence. Regarding design, the game genres arcade minigames, adventure worlds, and social simulations are used, in this order. For implementation, these serious games usually require sessions of 1 hour and are most often played using a PC. Moreover, the common evaluation tools are normalized questionnaires that measure acquisition of skills or reduction of symptoms. Most studies collect and compare these data before and after the participants play. The results show that more awareness and detection games are needed, as well as games that mix the awareness, prevention, detection, and therapy applications. In addition, games for depression and anxiety should equally target all age ranges. For future research, the development and evaluation of serious games should be standardized, so the implementation of serious games as tools would advance. The games should always offer support while playing, in addition to collecting data on participant behavior during the game to better analyze their learning. Furthermore, there is an open line regarding the use of virtual reality for these games due to the capabilities offered by this technology.

Serious games have to meet certain characteristics relating to gameplay and educational content to be effective as educational tools. There are some models that evaluate these aspects, but they usually lack a good balance between both ludic and learning requirements, and provide no guide for the design of new games. This study develops the Gaming Educational Balanced (GEB) Model which addresses these two limitations. GEB is based on the Mechanics, Dynamics and Aesthetics framework and the Four Pillars of Educational Games theory. This model defines a metric to evaluate serious games, which can also be followed to guide their subsequent development. This rubric is tested with three indie serious games developed using different genres to raise awareness of mental illnesses. This evaluation revealed two main issues: the three games returned good results for gameplay, but the application of educational content was deficient, due in all likelihood to the lack of expert educators participating in their development. A statistical and machine learning validation of the results is also performed to ensure that the GEB metric features are clearly explained and the players are able to evaluate them correctly. These results underline the usefulness of the new metric tool for identifying game design strengths and weaknesses. Future works will apply this metric to more serious games to further test its effectiveness and to guide the design of new serious games.