Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Agents, Believability and Embodiment in Advanced Learning Environments

On the World Wide Web we see a growing number of general HCI interfaces, interfaces to educational or entertainment systems, interfaces to professional environments, etc., where an animated face, a cartoon character or a human-like virtual agent has the task to assist the user, to engage the user into a conversation or to educate the user. What can be said say about the effects a human-like agent has on a student's performance? We discuss agents, their intelligence, embodiment and interaction modalities. In particular, we introduce viewpoints and questions about roles embodied agents can play in educational environment

Role of Virtual Reality in Geography and Science Fieldwork Education

Fieldwork has a long tradition in geography, and in certain sciences, notably geology, biology and environmental sciences. Fieldwork involves leaving the classroom and engaging in learning and teaching through first-hand experience of phenomena in outdoor settings. Exploration in natural habitats introduces students to the complexity and unpredictability of the real world, stimulates their curiosity, and increases their interest in scientific inquiry. However, over the last decade, there has been a decline in field-study opportunities in schools. This policy paper describes the first extensive user-centered research programme into the role of technology-enabled virtual field trips as a means for improving the effectiveness of the outdoor fieldwork experience. It draws on a year-long research project that investigated how Google Expeditions, a smartphone-driven mobile virtual reality application, bridges virtual fieldwork with physical field trips and facilitates inquiry-based fieldwork and experiential learning. It examines the role of Google Expeditions in primary and secondary school science and geography, outlining the opportunities and challenges of integrating mobile virtual reality in schools and the practical implications of our research for fieldwork education in further and higher education

The Role of Spatial Ability in Learning with Virtual Reality: A Literature Review

No research has systematically reviewed the role of spatial ability in virtual reality (VR) learning. This has resulted in inefficiencies in educators’ ability to adopt personalized teaching strategies based on learners’ spatial ability to maximize the effectiveness of VR. Therefore, this study conducted a literature review on spatial ability in VR learning to provide researchers and educators with a comprehensive understanding of how spatial ability affects VR learning. After searching Scopus with keywords and applying inclusion and exclusion criteria, the researchers identified 30 relevant research articles for the review. This literature review mainly analyzed research trends, contexts, theories, methodologies, and findings from the identified articles. The contradictory role of spatial ability in VR learning was also summarized. Based on the literature analysis, this study identified research gaps and indicated directions for future research

Towards autonomous diagnostic systems with medical imaging

Democratizing access to high quality healthcare has highlighted the need for autonomous diagnostic systems that a non-expert can use. Remote communities, first responders and even deep space explorers will come to rely on medical imaging systems that will provide them with Point of Care diagnostic capabilities. This thesis introduces the building blocks that would enable the creation of such a system. Firstly, we present a case study in order to further motivate the need and requirements of autonomous diagnostic systems. This case study primarily concerns deep space exploration where astronauts cannot rely on communication with earth-bound doctors to help them through diagnosis, nor can they make the trip back to earth for treatment. Requirements and possible solutions about the major challenges faced with such an application are discussed. Moreover, this work describes how a system can explore its perceived environment by developing a Multi Agent Reinforcement Learning method that allows for implicit communication between the agents. Under this regime agents can share the knowledge that benefits them all in achieving their individual tasks. Furthermore, we explore how systems can understand the 3D properties of 2D depicted objects in a probabilistic way. In Part II, this work explores how to reason about the extracted information in a causally enabled manner. A critical view on the applications of causality in medical imaging, and its potential uses is provided. It is then narrowed down to estimating possible future outcomes and reasoning about counterfactual outcomes by embedding data on a pseudo-Riemannian manifold and constraining the latent space by using the relativistic concept of light cones. By formalizing an approach to estimating counterfactuals, a computationally lighter alternative to the abduction-action-prediction paradigm is presented through the introduction of Deep Twin Networks. Appropriate partial identifiability constraints for categorical variables are derived and the method is applied in a series of medical tasks involving structured data, images and videos. All methods are evaluated in a wide array of synthetic and real life tasks that showcase their abilities, often achieving state-of-the-art performance or matching the existing best performance while requiring a fraction of the computational cost.Open Acces

Computational aerodynamics and artificial intelligence

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics