Virtual Reality Environment Research Articles

The brain hierarchically represents the past and future during multistep anticipation.

Memory for temporal structure enables both planning of future events and retrospection of past events. We investigated how the brain flexibly represents extended temporal sequences into the past and future during anticipation. Participants learned sequences of environments in immersive virtual reality. Pairs of sequences had the same environments in a different order, enabling context-specific learning. During fMRI, participants anticipated upcoming environments multiple steps into the future in a given sequence. Temporal structure was represented in the hippocampus and across higher-order visual regions (1) bidirectionally, with graded representations into the past and future and (2) hierarchically, with further events into the past and future represented in successively more anterior brain regions. In hippocampus, these bidirectional representations were context-specific, and suppression of far-away environments predicted response time costs in anticipation. Together, this work sheds light on how we flexibly represent sequential structure to enable planning over multiple timescales.

Voice user interfaces for effortless navigation in medical virtual reality environments

In various situations, such as clinical environments with sterile conditions or when hands are occupied with multiple devices, traditional methods of navigation and scene adjustment are impractical or even impossible. We explore a new solution by using voice control to facilitate interaction in virtual worlds to avoid the use of additional controllers. Therefore, we investigate three scenarios: Object Orientation, Visualization Customization, and Analytical Tasks and evaluate whether natural language interaction is possible and promising in each of these scenarios. In our quantitative user study participants were able to control virtual environments effortlessly using verbal instructions. This resulted in rapid orientation adjustments, adaptive visual aids, and accurate data analysis. In addition, user satisfaction and usability surveys showed consistently high levels of acceptance and ease of use. In conclusion, our study shows that the use of natural language can be a promising alternative for the improvement of user interaction in virtual environments. It enables intuitive interactions in virtual spaces, especially in situations where traditional controls have limitations.

Spatial Orientation Assessment in the Elderly: A Comprehensive Review of Current Tests.

Spatial orientation and navigation are complex cognitive functions that integrate sensory information, attention, and memory, enabling individuals to locate themselves in their environment. These abilities decline with age, signaling cognitive impairment in neurological patients, and significantly limit the autonomy of the elderly. Current neuropsychological assessments fall short in accurately measuring everyday wayfinding abilities, particularly in borderline cases of cognitive decline. This paper reviews various neuropsychological assessments, including Benton's Judgment of Line Orientation Test, the Almeria Spatial Memory Recognition Test, the Spatial Span subtest from the Wechsler Memory Scale, and the Spatial Orientation in Immersive Virtual Environment Maze Test, evaluating their effectiveness in delineating spatial orientation and navigation skills. The review identifies significant gaps in the validity and reliability of these tests, particularly in their shortened versions, and highlights the potential of virtual reality environments as promising tools for improving diagnostic precision. The findings underscore the need for further research to refine these tools, ensuring they accurately capture cognitive decline and improve the differential diagnosis of neurodegenerative conditions like Alzheimer's disease. Such advancements hold promise for enhancing the quality of care and autonomy for the elderly.

MODEL IMPLEMENTATION AND COMPREHENSIVE STUDY ON VISUAL AIR TYPING SYSTEMS

This paper explores the implementation and applications of visual air typing systems, focusing on fingertip detection and text recognition to provide an alternative to the traditional input methods. Although models with similar functionality exist, our study emphasizes how such systems can offer practical solutions for users with visual impairments and enhance user experience in virtual reality (VR) environments. By detecting fingertip movements and translating them into written words, such models present a seamless and intuitive user experience. For individuals with visual impairments, traditional keyboards can be challenging, and sign language lacks uniformity across different regions. An air typing system addresses these issues by offering a consistent text input method. Additionally, in VR applications where traditional keyboards are impractical, it enables more natural and efficient text entry. This paper provides a comprehensive review of existing air typing technologies, detailing their strengths and limitations. We discuss the potential future directions for air typing systems, highlighting the need for more inclusive and versatile input methods. Our findings indicate that air typing can significantly enhance user interaction in diverse environments, making it a valuable alternative to traditional keyboards. Overall, this study underscores the practical applications and benefits of air typing systems, particularly for visually impaired users and VR contexts, contributing to advancing more inclusive technological solutions.

The IntegraPark Study: An Opportunity to Facilitate High-Intensity Exercise with Immersive Virtual Reality in Parkinson's Disease Patients.

high-intensity exercise is a feasible and effective modality in the early stages of Parkinson's disease (PD). However, there are only a few studies that have carried out this type of intervention in customizable immersive virtual reality (IVR) environments. We explore the feasibility and effects of IVR-based high-intensity training through rowing and cycling exercises on the functional capacity, quality of life, and progression of PD. a total of 12 participants (61.50 ± 10.49 years old; 41.7% female, 58.3% male; stages I-III) were part of the study, which consisted of interventions of rowing and cycling in an IVR commercial exergame (25 min; twice per week for 14 weeks). The main variables measured were functional capacity, handgrip strength, functional mobility (TUG), functional lower-limb strength (FTSST), aerobic capacity (2-min step test), quality of life (PDQ-39), and Parkinson's disease progression (MDS-UPDRS). the results showed a general improvement in handgrip strength in both hands (p = 0.008; d = 0.28), FTSST (p = 0.029; d = 0.96), and TUG times (p = 0.152; d = 0.22). Aerobic capacity, assessed by a 2-min step test, showed enhanced scores (p = 0.031; d = 0.78). Related to the PDQ-39, all dimensions of the scale were enhanced, highlighting activities of daily living (p = 0.047; d = 0.29) and bodily discomfort (p = 0.041; d = 0.37). Finally, the main symptoms of the disease were reduced, with an improvement in the parameters that show a better incidence of disease progression, such as Part IA and IB (p = 0.013; d = 0.29 and p = 0.021; d = 0.25, respectively), Part II (p = 0.021; d = 0.23), Part III (p = 0.040; d = 0.39), and Part IV (p = 0.013; d = 0.39). the therapeutic exercise (rowing and cycling), when carried out at a high intensity and in a personalized IVR scenario, appear to be a feasible and safe modality for patients with stages I-III of PD, improving their functional capacity, quality of life, and disease progression.

RETRACTED: Huamanchahua et al. Kinematic of the Position and Orientation Synchronization of the Posture of a n DoF Upper-Limb Exoskeleton with a Virtual Object in an Immersive Virtual Reality Environment. Electronics 2021, 10, 1069

The journal is retracting the article, “Kinematic of the Position and Orientation Synchronization of the Posture of a n DoF Upper-Limb Exoskeleton with a Virtual Object in an Immersive Virtual Reality Environment” [...]

Auxiliary diagnostic method of Parkinson’s disease based on eye movement analysis in a virtual reality environment

Eye movement dysfunction is one of the non-motor symptoms of Parkinson’s disease (PD). An accurate analysis method for eye movement is an effective way to gain a deeper understanding of the nervous system function of PD patients. However, currently, there are only a few assistive methods available to help physicians conveniently and consistently assess patients suspected of having PD. To solve this problem, we proposed a novel visual behavioral analysis method using eye tracking to evaluate eye movement dysfunction in PD patients automatically. This method first provided a physician task simulation to induce PD-related eye movements in Virtual Reality (VR). Subsequently, we extracted eye movement features from recorded eye videos and applied a machine learning algorithm to establish a PD diagnostic model. Then, we collected eye movement data from 66 participants (including 22 healthy controls and 44 PD patients) in a VR environment for training and testing during visual tasks. Finally, on this relatively small dataset, the results reveal that the Support Vector Machine (SVM) algorithm has better classification potential.

Research and Evaluation of Multi-Sensory Design of Product Packaging Based on VR Technology in Online Shopping Environment

The development and application of virtual reality (VR) technology significantly enhances consumer immersion. Exploring a multi-sensory evaluation model for virtual packaging is valuable for integrating VR technology with packaging. This study developed a multi-sensory evaluation model for virtual packaging using the analytic hierarchy process (AHP). Eye-tracker experimentation was conducted to identify consumer attention indicators when interacting with virtual packaging. These indicators were quantified using Saaty’s nine-level importance scale and expert input, resulting in a comprehensive multi-sensory evaluation model. Subsequently, a VR shopping system focused on potato chips and cola as packaging design objects. This system was evaluated using the established model, and the results were analyzed. Based on the findings, improvements were made, and the system was re-evaluated using the modified model. The post-improvement evaluation demonstrated significantly enhanced sensory experiences. These results validate that the developed evaluation model effectively guides multi-sensory design approaches for packaging within a VR environment.

Hierarchical Spherical Cross‐Parameterization for Deforming Characters

AbstractThe demand for immersive technology and realistic virtual environments has created a need for automated solutions to generate characters with morphological variations. However, existing approaches either rely on manual labour or oversimplify the problem by limiting it to static meshes or deformation transfers without shape morphing. In this paper, we propose a new cross‐parameterization approach that semi‐automates the generation of morphologically diverse characters with synthesized articulations and animations. The main contribution of this work is that our approach parameterizes deforming characters into a novel hierarchical multi‐sphere domain, while considering the attributes of mesh topology, deformation and animation. With such a multi‐sphere domain, our approach minimizes parametric distortion rates, enhances the bijectivity of parameterization and aligns deforming feature correspondences. The alignment process we propose allows users to focus only on major joint pairs, which is much simpler and more intuitive than the existing alignment solutions that involve a manual process of identifying feature points on mesh surfaces. Compared to recent works, our approach achieves high‐quality results in the applications of 3D morphing, texture transfer, character synthesis and deformation transfer.

Color and brightness at work: Shedding some light on mind wandering.

Occupational hazards are partly caused by the physical factors of the work environment, among which are ambient color and brightness, which can interfere with cognitive performance. Especially in modern work environments, performance relies heavily on cognitive functions such as attention, and an important factor in disrupting sustained attention is mind wandering (MW). This study aimed to investigate the effects of white and blue colors with two brightness levels on sustained attention and brain electrophysiology. A total of 20 participants were exposed to 4 different conditions (white and blue as color and 300 and 800lx as the brightness level) in separate blocks in a virtual reality environment in which a continuous performance test (CPT) was performed. The high brightness blue condition induced significant changes in sustained attention. MW network analysis showed a significant decrease in delta frequency band in the blue color condition with high brightness and beta decrease in the blue color condition with low brightness, whereas the activity of MW network increased when exposed to the white color condition. High-brightness blue light resulted in better sustained attention and decreased activity of MW-related neural regions. It is thus recommended that these results be taken into consideration in the interior design of educational settings and cars among other environments that require a high level and maintenance of cognitive functions, especially sustained attention.

Classification of Tri Pramana learning activities in virtual reality environment using convolutional neural network

<em><span lang="EN-GB">Tri Pramana</span></em><span lang="EN-GB"> as the local genius of Balinese society, is now adopted in the education system. This adaptation results in a Learning Cycle Model which essentially consists of three classes namely </span><em><span lang="EN-GB">Sabda Pramana</span></em><span lang="EN-GB"> (theoretical study), </span><em><span lang="EN-GB">Pratyaksa Pramana</span></em><span lang="EN-GB"> (direct observation), and </span><em><span lang="EN-GB">Anumana Pramana</span></em><span lang="EN-GB"> (practicum). In learning activities, it is difficult for educators to fully observe individuals to find out the most suitable learning model. Through Virtual Environment Technology, educators can observe students more freely through the recording of students' activities. However, in its implementation, manual analysis requires large resources. Deep Learning approach based on Convolutional Neural Network (CNN) is able to automate this analysis process through the classification ability of the image of the recorded learner activity. To produce a robust CNN model, this research compares four of the most commonly used architectures, namely ResNet-50, MobileNetV2, InceptionV3, and Xception. Each architecture is tuned using a combination of learning rate and batch size. Through a 512 x 512 resolution dataset with 70% training subset (4,541 images), 20% validation (1,296 images), and 10% test (652 images), the best ResNet model is obtained with a learning rate configuration of 1e-3 and batch size 64 with an accuracy of 99.39%, precision of 99.37%, and recall of 99.42%.</span>

P212 COMPARISON OF COLLEGE STUDENTS’ HEALTH METRICS DURING TRADITIONAL AND VIRTUAL REALITY BASED CYCLING

Background: Most college students do not meet the recommended physical activity guidelines. Novel technology such as virtual reality (VR) may be used to promote physical activity among sedentary students. Purpose: To investigate physiological changes in heart rate (HR), blood-pressure (BP) and rating of perceived exertion (RPE) among college students during stationary cycling with and without VR. Methods: Twenty-two college-students (7 M/15F; 18-30years) were recruited on campus. Students separately & randomly completed 2 separate cycling-sessions (with and without VR). The VZFit Ring Racer application with Meta Quest 2 VR Headset synced to a RENPHO smart AI bike was used for the VR session. HR, BP and RPE were assessed at 5-min intervals. Results: Only, RPE at 5-min interval was significant (p=0.032) between the non-VR and VR groups, which may be attributed to the fact that participants were focused more on the VR environment rather than the exercise session, potentially lowering their RPE. Conclusions: In this preliminary data, college students HR, BP & RPE were influenced during the VR session with RPE being lower. This suggests that VR may reduce the perception of effort for a given exercise intensity, and it could be used as a tool to engage students and promote physical activity, in an otherwise sedentary student population.

Adult zebrafish can learn Morris water maze-like tasks in a two-dimensional virtual reality system

Virtual reality (VR) has emerged as a powerful tool for investigating neural mechanisms of decision-making, spatial cognition, and navigation. In many head-fixed VRs for rodents, animals locomote on spherical treadmills that provide rotation information in two axes to calculate two-dimensional (2D) movement. On the other hand, zebrafish in a submerged head-fixed VR can move their tail to enable movement in 2D VR environment. This motivated us to create a VR system for adult zebrafish to enable 2D movement consisting of forward translation and rotations calculated from tail movement. Besides presenting the VR system, we show that zebrafish can learn a virtual Morris water maze-like (VMWM) task in which finding an invisible safe zone was necessary for the zebrafish to avoid an aversive periodic mild electric shock. Results show high potential for our VR system to be combined with optical imaging for future studies to investigate spatial learning and navigation.

An integrated approach to Bayesian weight regulations and multitasking learning methods for generating emotion-based content in the metaverse

This paper introduces an integrated model designed to analyze topics and sentiments in textual data simultaneously, recognizing their interdependence. By tackling challenges such as data scarcity, missing information, and biased distributions, the proposed model effectively captures the dynamic interactions between topics and sentiments within complex datasets. The method combines the use of Convolutional Neural Networks (CNN) for detecting topic-related patterns with a revised Recurrent Neural Network (RNN) to trace the emotional flow within contexts, leveraging the strengths of sequential data processing. These components are integrated within a Bayesian probability framework, modeling the conditional probabilities of sentences expressing specific sentiments and documents being associated with particular topics. The combined feature and state vectors from the CNN and revised RNN within this Bayesian setup enable precise classification and prediction of topics and sentiments. Furthermore, this paper explores innovative research avenues, including sentiment analysis in 3D virtual reality environments and the development of new algorithms that reflect content creation techniques in the metaverse, offering dynamic system construction. This integrated approach not only enhances data-driven decision-making but also unlocks new possibilities for advanced multi-text analysis.

Evaluating and Modeling the Effect of Frame Rate on Steering Performance in Virtual Reality.

Prior work has shown that frame rate significantly influences user behavior in fast-response tasks in 2D and 3D contexts. However, its impact on a steering task, which involves navigating an object along a path from the start to the end, remains relatively unexplored, especially in the context of virtual reality (VR). This task is considered a typical non-fast-response activity, as it does not demand rapid reactions within a limited time frame. Our work aims to understand and model users' steering behavior and predict movement time with different task complexities and frame rates in VR environments. We first conducted a user study to collect user behavior in a steering task with four factors: frame rate, path length, width, and radius of curvature. Based on the results, we then quantified the effects of frame rate and built two predictive models. Our models exhibited the best fit ([Formula: see text]) and over 17% improvement in prediction accuracy for movement time compared to existing models. Our models' robustness was further validated by applying them to predict steering performance with different VR tasks and frame rates. The two models keep the best predictability for both movement time and speed.

Pseudo-walking Sensation by Anteroposterior or Lateral Galvanic Vestibular Stimulation and Synchronous Foot-sole Vibrations.

The walking sensation is a result of the synthesis of multisensory inputs from various systems. The vestibular system, typically used for detecting acceleration, is a crucial component of the walking sensation. This study investigated the use of galvanic vestibular stimulation(GVS) to enhance the sensation of walking in virtual reality (VR) environments, particularly when users are seated and not engaged in active movements. GVS is a transcutaneous electric stimulation technique to evoke vestibular sensory responses and involves the application of a penetrating current to vestibular afferents. This study revealed that the pseudo-walking sensation can be intensified by applying lateral GVS. However, no difference was observed when it was synchronized with the walking rhythm represented by foot-sole vibration patterns. Furthermore, the study compares the effectiveness of lateral versus anterior-posterior GVS in enhancing walking sensations in VR. The findings provide novel perspectives on enhancing the VR walking experience through vestibular stimulation, even in scenarios in which the user is seated.

Impact of Immersion in Virtual Reality (VR) and Desktop Environments on Spatial Ability

Impact of Immersion in Virtual Reality (VR) and Desktop Environments on Spatial Ability

The Impact of Perceived Escapism on Visitor Experience and Engagement in Digital Exhibitions: Empirical Research Evidence from Sanxingdui

The evolution of digital technology offers virtual reality environment to escape and redefines visitor engagement in exhibitions. This study proposed a theoretical model that delineates the influence of perceived escapism on visitor engagement, encompassing various digital experience variables. Moreover, it categorizes digital exhibition engagement into two dimensions: breadth and depth of engagement. Focusing on the experience of viewing the Sanxingdui digital exhibition, an online survey was conducted and analyzed, with a sample of 464 participants in China. The findings of this investigation unveil several key insights: (a) Perceived escapism exhibits a positive correlation with visitor engagement; (b) Mediating effects of entertainment, immersion, and flow experience exist between perceived escapism and visitor engagement, with flow experience emerging as the most significant mediator; (c) Digital literacy negatively moderates the mediating effect of perceived escapism and immersion experience. Consequently, enhancing visitor engagement in digital exhibitions can be achieved by exhibition designers considering the diverse patterns of social interaction among visitors and creating a personalized and interactive escape environment.

Examining the effect of locomotion techniques on virtual prototype assessment: Gaze analysis using a Head-Mounted Display

Improvements in the performance and graphical quality of Head-Mounted Displays (HMDs) have led to their increasing use in Virtual Reality (VR) for product presentation and virtual prototype (VP) evaluations. Various locomotion techniques in VR make it possible to move through a virtual scenario and approach the VP for evaluation purposes. The integration of eye-tracking devices into recent HMDs allows the trajectory and gaze behavior of observers to be reported during the evaluation, often more objectively than self-report questionnaires. However, very few studies have used physiological measures for the evaluation of products embedded in VR environments. Therefore, this paper offers a study in which 95 people evaluated three VPs of street furniture presented in their environment of use using Meta Quest Pro headset and explored through teleport and natural walking. The influence of the locomotion techniques on the ratings recorded using a semantic differential, sense of presence, cybersickness, and the role of eye-tracking in understanding gaze behavior while evaluating products' Areas of Interest (AOIs), are investigated. This study found no evidence that the way of approaching the product influences the evaluation of some of its features, overall product evaluation, confidence in responses, sense of presence, or cybersickness differently. On the other hand, this work evidences that the locomotion technique had an impact on how the user approached the products, which could significantly influence the viewing time of some AOIs. The study revealed that the most observed AOIs coincided with those parts closely related to important features, generally located at the top of the products, so paying special attention to these parts when designing and evaluating similar VPs is recommended.

Using SIM-TLX to investigate the potential impacts on cognitive load while undertaking tasks in a virtual workplace

PurposeThe ability to measure cognitive load in the workplace provides several opportunities to improve workplace learning. In recent years, virtual reality (VR) has seen an increase in use for training and learning applications due to improvements in technology and reduced costs. This study aims to focus on the use of simulation task load index (SIM-TLX), a recently developed self-reported measure of cognitive load for virtual environments to measure cognitive load while undertaking tasks in different environments.Design/methodology/approachThe authors conducted a within-subject design experiment involving 14 participants engaged in digit-recall n-back tasks (1-back and 2-back) in two VR environments: a neutral grey environment and a realistic industrial ozone facility. Cognitive load was then assessed using the SIM-TLX.FindingsThe findings revealed higher task difficulty for the 2-back task due to higher mental demand. Furthermore, a notable interaction emerged between cognitive load and different virtual environments.Research limitations/implicationsThis study relied solely on an n-back task and SIM-TLX self-report measure to assess cognitive load. Future studies should consider including ecologically valid tasks and physiological measurement tools such as eye-tracking to measure cognitive load.Practical implicationsIdentifying cognitive workload sources during VR tasks, especially in complex work environments, is considered beneficial to the application of VR training aimed at improving workplace learning.Originality/valueThis study provides unique insights into measuring cognitive load from various sources as defined by the SIM-TLX sub-scales to investigate the impact of simulated workplace environments.