Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education

Fine motor skills allow to carry out the execution of crucial tasks in people's daily lives, increasing their independence and self-esteem. Among the alternatives for working these skills, immersive environments are found providing a set of elements arranged to have a haptic experience through gestural control devices. However, generally, these environments do not have a mechanism for evaluation and feedback of the exercise performed, which does not easily identify the objective's fulfillment. For this reason, this study aims to carry out a comparison of image recognition methods such as Convolutional Neural Network (CNN), K-Nearest Neighbor (K-NN), Support Vector Machine (SVM) and Decision Tree (DT), for the purpose of performing an evaluation and feedback of exercises. The assessment of the techniques is carried out using images captured from an immersive environment, calculating metrics such as confusion matrix, cross validation and classification report. As a result of this process, it was obtained that the CNN model has a better supported performance in 82.5% accuracy, showing an increase of 23.5% compared to SVM, 30% compared to K-NN and 25% compared to DT. Finally, it is concluded that in order to implement a method of evaluation and feedback in an immersive environment for academic training in the first school years, a low margin of error must be taken in the percentage of successes of the image recognition technique implemented, to ensure the proper development of these skills considering their great importance in childhood

Editor’s Note

The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. After its recent tenth anniversary, the journal has achieved an important milestone. From 2015 to 2018 IJIMAI was indexed at Web of Science through Emerging Science Citation Index. This meant a great increase in visibility and number of received papers. This year, Clarivate Analytics has accepted the inclusion of IJIMAI in the Journal Citation Reports. Specifically, IJIMAI is being indexed and abstracted in Science Citation Index Expanded, Journal Citation Reports/Science Edition and Current Contents®/Engineering Computing and Technology. The Web of Science Categories in which IJIMAI is included are “Computer Science, Artificial Intelligence” and “Computer Science, Interdisciplinary Applications”. This way, IJIMAI is indexed in Science Citation Index Expanded beginning with vol.4(3) March 2017 so that the journal will be listed in the 2019 Journal Citation Reports with a Journal Impact Factor when released in June 2020. Given this great achievement, IJIMAI Editorial Board has to thank authors for all the papers sent and all the papers published, as well as reviewers for their support to obtain high-quality in papers, and specially our readers because without them this milestone would not have been possible. The present regular issue includes research works based on different AI methods such as convolutional neural networks, genetic algorithms, lightning attachment procedure optimization, or those of multi-agent systems. These methods are applied into various fields as video surveillance, gesture recognition, sentiment analysis, territory planning, search engines, epidemiological surveillance or robotics

Motor learning and transfer between real and virtual environments in young people with autism spectrum disorder: a prospective randomized cross over controlled trial

Autism spectrum disorder (ASD) is associated with persistent deficits in social communication and social interaction, including impaired multisensory integration which might negatively impact cognitive and motor skill performance, and hence negatively affect learning of tasks. Considering that tasks in virtual environment may provide an engaging tool as adjuncts to conventional therapies, we set out to compare motor performance between young people with ASD and a typically developing (TD) control group that underwent coincident timing tasks based on Kinect (no physical contact) and on Keyboard (with physical contact) environments. Using a randomized repeated cross-over controlled trial design, fifty young people with ASD and fifty with TD, matched by age and sex were divided into subgroups of 25 people that performed the two first phases of the study (acquisition and retention) on the same device – real or virtual – and then switched to the other device to repeat acquisition and retention phases and finally switched on to a touch screen (transfer phase). Results showed that practice in the virtual task was more difficult (producing more errors), but led to a better performance in the subsequent practice in the real task, with more pronounced improvement in the ASD as compared to the TD group. It can be concluded that the ASD group managed to transfer the practice from a virtual to a real environment, indicating that virtual methods may enhance learning of motor and cognitive skills. A need for further exploration of its effect across a number of tasks and activities is warranted.

An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training

Applications of Virtual Reality

Information Technology is growing rapidly. With the birth of high-resolution graphics, high-speed computing and user interaction devices Virtual Reality has emerged as a major new technology in the mid 90es, last century. Virtual Reality technology is currently used in a broad range of applications. The best known are games, movies, simulations, therapy. From a manufacturing standpoint, there are some attractive applications including training, education, collaborative work and learning. This book provides an up-to-date discussion of the current research in Virtual Reality and its applications. It describes the current Virtual Reality state-of-the-art and points out many areas where there is still work to be done. We have chosen certain areas to cover in this book, which we believe will have potential significant impact on Virtual Reality and its applications. This book provides a definitive resource for wide variety of people including academicians, designers, developers, educators, engineers, practitioners, researchers, and graduate students

Performance Factors in Neurosurgical Simulation and Augmented Reality Image Guidance

Virtual reality surgical simulators have seen widespread adoption in an effort to provide safe, cost-effective and realistic practice of surgical skills. However, the majority of these simulators focus on training low-level technical skills, providing only prototypical surgical cases. For many complex procedures, this approach is deficient in representing anatomical variations that present clinically, failing to challenge users’ higher-level cognitive skills important for navigation and targeting. Surgical simulators offer the means to not only simulate any case conceivable, but to test novel approaches and examine factors that influence performance. Unfortunately, there is a void in the literature surrounding these questions. This thesis was motivated by the need to expand the role of surgical simulators to provide users with clinically relevant scenarios and evaluate human performance in relation to image guidance technologies, patient-specific anatomy, and cognitive abilities. To this end, various tools and methodologies were developed to examine cognitive abilities and knowledge, simulate procedures, and guide complex interventions all within a neurosurgical context. The first chapter provides an introduction to the material. The second chapter describes the development and evaluation of a virtual anatomical training and examination tool. The results suggest that learning occurs and that spatial reasoning ability is an important performance predictor, but subordinate to anatomical knowledge. The third chapter outlines development of automation tools to enable efficient simulation studies and data management. In the fourth chapter, subjects perform abstract targeting tasks on ellipsoid targets with and without augmented reality guidance. While the guidance tool improved accuracy, performance with the tool was strongly tied to target depth estimation – an important consideration for implementation and training with similar guidance tools. In the fifth chapter, neurosurgically experienced subjects were recruited to perform simulated ventriculostomies. Results showed anatomical variations influence performance and could impact outcome. Augmented reality guidance showed no marked improvement in performance, but exhibited a mild learning curve, indicating that additional training may be warranted. The final chapter summarizes the work presented. Our results and novel evaluative methodologies lay the groundwork for further investigation into simulators as versatile research tools to explore performance factors in simulated surgical procedures

Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder

Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR). Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) are good predictors of eating disorder behavior. Our second study uses a hierarchical clustering algorithm to find patterns in the dataset that were previously not considered. We found four clusters that may highlight the unique differences between participants who had positive body image versus participants who had negative body image. The final chapter presents a case study with a specific VR tool, Bob’s Fish Shop, and users with ASD and Attention Deficit Hyperactivity Disorder (ADHD). We hypothesize that, through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding. Through the implementation of various machine learning algorithms and programs, we can study the human experience in a way that has never been done. We can account for neurodiverse populations and assist them in ways that are not only helpful but also educational

Implementation of Virtual Reality (VR) simulators in Norwegian maritime pilotage training

With millions of tons of cargo transported to and from Norwegian ports every year, the maritime waterways in Norway are heavily used. The high consequences of accidents and mishaps require well-trained seafarers and safe operating practices. The normal crews of vessels are supported by the Norwegian Coastal Administration (NCA) pilot service when operating vessels not meeting specific regulations. Simulator training is used as part of the toolset designed to educate, train, and advance the knowledge of maritime pilots in order to improve their operability. The NCA is working on an internal project to distribute Virtual Reality (VR) simulators to selected pilot stations along the coast and train and familiarize maritime pilots with the tool. There has been a lack of research on virtual reality simulators and how they are implemented in maritime organizations. The goal of this research is to see if a VR-simulator can be used as a training tool within the Norwegian Coastal Administration's pilot service. Furthermore, the findings of this study contribute to the understanding of VR-simulators in the field of Maritime Education and Training (MET). The thesis is addressing two research questions: 1. Is the Virtual Reality training useful in the competence development process of Norwegian maritime pilots? 2. How can the Virtual Reality simulators improve training outcomes of today’s maritime pilot education? The data gathered from the systematic literature review corresponds to the findings of the interviews. Considering the similarities with previous study findings from sectors such as healthcare, construction, and education, it is concluded that the results of the interviews can be generalized. For maritime pilots, the simulator offers recurrent scenario-based training and a high level of immersion. Pilots can learn at home, onboard a vessel, at the pilot station, and in group settings thanks to the system's mobility and user-friendliness. In terms of motivation and training effectiveness, the study finds that VR-simulators are effective and beneficial. The technology received positive reviews from the pilots. The simulator can be used to teach both novice and experienced maritime pilots about new operations, larger tonnage, and new operational areas, according to the findings of the research. After the NCA has utilized VR-simulators for some time, additional research may analyze the success of VR-simulators using a training evaluation study and investigate the impact of VR-training in the organization

Literacy for digital futures : Mind, body, text

The unprecedented rate of global, technological, and societal change calls for a radical, new understanding of literacy. This book offers a nuanced framework for making sense of literacy by addressing knowledge as contextualised, embodied, multimodal, and digitally mediated. In today’s world of technological breakthroughs, social shifts, and rapid changes to the educational landscape, literacy can no longer be understood through established curriculum and static text structures. To prepare teachers, scholars, and researchers for the digital future, the book is organised around three themes – Mind and Materiality; Body and Senses; and Texts and Digital Semiotics – to shape readers’ understanding of literacy. Opening up new interdisciplinary themes, Mills, Unsworth, and Scholes confront emerging issues for next-generation digital literacy practices. The volume helps new and established researchers rethink dynamic changes in the materiality of texts and their implications for the mind and body, and features recommendations for educational and professional practice