Learning Space in Virtual Environments

Advances in Virtual Reality (VR) systems, Intelligent Tutoring Systems and Agent Technology make it possible to design and develop Virtual Training Environments (VTEs), where the trainees can immerse themselves and interact directly with the learning domain. This paper presents the Interaction Specification Workspace (ISW) architecture for the specification and design of virtual environments for training purposes. ISW architecture provides the interaction designer with the capability to specify the training interaction with the virtual environment using the Virtual Reality Multi Flow Graph (VR-MFG) as the underlying interaction specification model. ISW implements a design space, where the processes of interaction specification and design of VTEs take place inside a three-dimensional virtual environment, the objects of which are tools by themselves. Inside this workspace, the designer can associate the abstract objects (the components of the VR-MFG) with “actual” objects of the target virtual environment (kept in a “world” database) and apply a number of agent templates with training capabilities.

Enhancing virtual environment spatial awareness training and transfer through tactile and vestibular cues

Training in virtual environments: putting theory into practice

Task-specific training in virtual environments (VEs) can provide practice for skills that are transferred to real-world settings. The present study examined skill acquisition and retention of a non-injured population performing a sensorimotor navigation task in the Computer Assisted Rehabilitation Environment (CAREN). Seventeen subjects participated twice weekly for 6 weeks, with follow-up visits at 3-month intervals for one year. Subjects performed a navigation task, where they drove a virtual boat through a scene using weight shifting and body movement. Subjects improved over time on all outcome measures. A significant effect was observed for visit number on total score, time to complete the task, number of buoys navigated successfully, and number of penalties incurred. No differences were observed between the last training visit and any follow-up visit. Task-specific training in immersive VEs may be effective for warfighter operational skills training and rehabilitation of wounded warriors, by utilizing tasks that lead to long-term retention.

Concepts of spatial navigation rest on the idea of landmarks, which are immobile features or objects in the environment. However, behaviorally relevant objects or fellow humans are often mobile. This raises the question of how the presence of human agents influences spatial exploration and knowledge acquisition. Here, we investigate exploration and performance in subsequent spatial tasks within a virtual environment containing numerous human avatars. In the exploration phase, agents had a locally limited effect on navigation. They prompted participants to revisit locations with agents during their initial exploration without significantly altering overall exploration patterns or the extent of the area covered. However, agents and buildings competed for visual attention. When spatial recall was tested, pointing accuracy toward buildings improved when participants directed their attention to the buildings and nearby agents. In contrast, pointing accuracy for agents showed weaker performance and did not benefit from visual attention directed toward the adjacent building. Contextual agents and incongruent agent-environment pairings further enhanced pointing accuracy, revealing that violations of expectations by agents can significantly shape navigational knowledge acquisition. Overall, agents influenced spatial exploration by directing attention locally, with the interaction between agent salience and environmental features playing a key role in shaping navigational knowledge acquisition.

As one of the most expensive and frequently used resources, cranes play an essential role in the operation of activities on construction sites. They require certified operators whose knowledge of codes and regulations is essential for safe and productive lift tasks. Blind spots pose a significant issue for crane operators as they limit the crane operator’s field-of-view that potentially slows down execution of such tasks. Often collaboration between crane operators and ground personnel is limited and requires advanced communication techniques, such as radios, to resolve the limited visibility issue. Although visualization technology has been widely adopted in practice to enhance visibility, education and training programs related to cranes lack multi-user collaboration environments. This paper presents an approach towards developing a virtual training environment that allows multiple subjects to participate in lifting operations cooperatively. Through a 3D real-time immersive user visualization interface users are able to perform hands-on tasks such as operating cranes and directing blind lifts. In addition, geometry constraints and safety hazards are introduced to augment the simulation-fidelity of the virtual training environment. A test involving two users was conducted and the results show that the proposed approach can effectively simulate collaborative tasks in crane lifting operations. This study has potential to enhance the productivity and safety of crane lifting operations.

Evaluating the effects of collaboration and competition in navigation tasks and spatial knowledge acquisition within virtual reality environments

Objective. This study explored the classification of electroencephalography (EEG) signals to assess changes in neural activity as individuals performed a training task in a virtual environment simulator. Commonly, task behavior and perception are used to assess a trainee’s ability to perform a task, however, changes in cognition are not usually measured and could be important to provide a true indication of an individual’s level of knowledge or skill. Approach. In this study, 15 participants acquired spatial knowledge via 60 navigation trials (divided into ten blocks) in a novel virtual environment. Time performance, perceived certainty, and EEG signal data were collected. Main results. A significant increase in alpha power and classification accuracy of EEG data from block 1 against blocks 2–10 was observed and stabilized after block 7, while time performance and perceived certainty measures improved and stabilized after block 5 and 6, respectively. Significance. Results suggest that changes in neural activity, which may reflect an increase in cognitive efficiency, could provide additional insight beyond time performance and perceived certainty.

We present in this paper researches focused on specification and production of virtual training environments. The use of Virtual Environments (VE) for training is strongly stimulated by important needs of training on sensitive equipment. But the development of such applications is generally done in new projects, where the reusability of existing developments is a major issue. We present here a platform of development, based on internal models that have been designed to achieve this reusability and standardization for the efficient development of new virtual environments. We defined a new generic model named STORM, used to describe reusable behaviors for 3D objects and reusable interactions between those objects or with humans. We also defined a scenario language named LORA, which allow non-computer scientists author various and complex sequences of tasks in a virtual scene. Based on those models, as an industrial validation with Nexter-Group, we have created 20 operational scenarios of maintenance virtual training on military equipments. We are also begining to create collaborative environments, where virtual and real humans have to collaborate to achieve their procedures.

This study is concerned with semantic modelling of virtual environments (VEs). A semantic model of a VE provides an abstract and high level representation of main aspects of the environment: ontological structures, behaviours and interactions of entities, etc. Furthermore, such a semantic model can be explored by artificial agents to exhibit human-like behaviours or to assist users in the VE. Previous research focused on formalising a knowledge layer that is a conceptual representation of scene content or application's entities. However, there still lacks of a semantic representation of spatial knowledge. This paper proposes to integrate a semantic model of directional knowledge into VEs. Such a directional model allows to specify relationships such as left, right, above or north, south that are critical in many applications of VEs (e.g., VEs for training, navigation aid systems). We focus particularly on modelling, computing, and visualising directional relationships. First, we propose a theoretical model of direction in VEs that enables the specification of direction both from a first- and third-person perspective. Second, we propose a generic architecture for modelling direction in VEs using a meta-modelling approach. Directional relationships are described in a qualitative manner and at a conceptual level, and thus are abstract from metrical details of VEs. Finally, we show how our semantic model of direction can be used in a cultural heritage application to specify behaviours of artificial agents and to visualise directional constraints.

In this study, we examined the perception and understanding of spatial volumes within immersive and non-immersive virtual environments by comparison with representation using conventional media. We examined the relative effectiveness of these conditions in enabling the translation to a tangible representation, through a series of design experiments. Students experienced, assessed, and analysed spatial relationships of volumes and spaces and subsequently constructed real models of these spaces. The goal of our study is to identify how designers perceive space in Virtual Environments (VEs). We explore issues of quality, accuracy and understanding of reconstructing architectural space and forms. By comparison of the same spatial performance task undertaken within a Head Mounted Display, screen-based and real 2D environment, we are able to draw some conclusions about spatial understanding in immersive VE activity.

This study developed and tested a research model which examined the impact of user perceptions of self-efficacy (SE) and virtual environment (VE) efficacy on the effectiveness of VE training systems. The model distinguishes between the perceptions of one's own capability to perform trained tasks effectively and the perceptions of system performance, regarding the established parameters from literature. Specifically, the model posits that user perceptions will have positive effects on task performance and memory. Seventy-six adults participated in a VE in a controlled experiment, designed to empirically test the model. Each participant performed a series of object assembly tasks. The task involved selecting, rotating, releasing, inserting and manipulating 3D objects. Initially, the results of factor analysis demonstrated dimensionality of two user perception measures and produced a set of empirical validated factors underlining the VE efficacy. The results of regression analysis revealed that SE had a significant positive effect on perceived VE efficacy. No significant effects were found of perceptions on performance and memory. Furthermore, the study provided insights into the relationships between the perception measures and performance measures for assessing the efficacy of VE training systems. The study also addressed how well users learn, perform, adapt to and perceive the VE training, which provides valuable insight into the system efficacy. Research and practical implications are presented at the end of the paper.

The use of conversational agents to support team-based training in virtual environments is a nascent area of research within the Computer Supported Collaborative Work (CSCW) and Conversational User Interface (CUI) communities. To support this research, we developed a Wizard of Oz (WoZ) system enabling experimenters to play the role of the conversational agent who must work with a team of three human participants to herd together a group of autonomous robots (target agents) within a virtual task environment. The experimenter can select from one of several tailored responses. The aim of this environment is to determine whether the identified responses facilitate effective team performance and to collect training data to build a conversational agent capable of engaging in team based virtual learning environments.

BackgroundPattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device.MethodsHere we present an engaging and interactive virtual reality environment for optimal training of a myoelectric controller. Using this tool, we evaluated the importance of training a classifier actively (i.e., moving the residual limb during data collection) compared to passively (i.e., maintaining the limb in a single, neutral orientation), and whether computational adaptation through serious gaming can improve performance.ResultsWe found that actively trained classifiers performed significantly better than passively trained classifiers for non-amputees (P < 0.05). Furthermore, collecting data passively with minimal instruction, paired with computational adaptation in a virtual environment, significantly improved real-time performance of myoelectric controllers.ConclusionThese results further support previous work which suggested active movements during data collection can improve pattern recognition systems. Furthermore, adaptation within a virtual guided serious game environment can improve real-time performance of myoelectric controllers.

Spatial thinking and spatial orientation skills are involved in tasks related to the recognition of landforms, mapping, spatial interpretation, and landscape analysis, and can be developed with specific training. Game engines can facilitate the creation of 3D virtual landforms and provide powerful rendering engines for the graphical representation of landscapes from a first-person perspective. In the present research, 27 engineering students participated in a workshop in a first-person virtual environment using landforms created with a game engine. The Spatial Thinking Ability Test and the Perspective Taking-Spatial Orientation Test measured improvement in spatial thinking and spatial orientation as a result of this workshop. The gain in spatial thinking (8.31%) is within the range observed in previous research in the field of geography using a web-based GIS strategy (7.31%–10.00%). The gain in Spatial Orientation skill (15.76%) is comparable with previous research using both first-person strategies based in urban virtual environments (14.23%), and Spatial Data Infrastructures (gains between 21.17% and 21.34%). Participants with better self-reported sense of direction had better performance on the spatial orientation test.