Earthquake Response Drill Simulator based on a 3-DOF Motion base in Augmented Reality

The visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios.

Three-dimensional sound's effectiveness in virtual reality (VR) environments has been widely studied. However, due to the big differences between VR and augmented reality (AR) systems in registration, calibration, perceptual difference of immersiveness, navigation, and localization, it is important to develop new approaches to seamlessly register virtual 3-D sound in AR environments and conduct studies on 3-D sound's effectiveness in AR context. In this paper, we design two experimental AR environments to study the effectiveness of 3-D sound both quantitatively and qualitatively. Two different tracking methods are applied to retrieve the 3-D position of virtual sound sources in each experiment. We examine the impacts of 3-D sound on improving depth perception and shortening task completion time. We also investigate its impacts on immersive and realistic perception, different spatial objects identification, and subjective feeling of human presence and collaboration. Our studies show that applying 3-D sound is an effective way to complement visual AR environments. It helps depth perception and task performance, and facilitates collaborations between users. Moreover, it enables a more realistic environment and more immersive feeling of being inside the AR environment by both visual and auditory means. In order to make full use of the intensity cues provided by 3-D sound, a process to scale the intensity difference of 3-D sound at different depths is designed to cater small AR environments. The user study results show that the scaled 3-D sound significantly increases the accuracy of depth judgments and shortens the searching task completion time. This method provides a necessary foundation for implementing 3-D sound in small AR environments. Our user study results also show that this process does not degrade the intuitiveness and realism of an augmented audio reality environment

An increasingly large amount of data from the physical world has been collected, digitized, and stored. To communicate such data to general public effectively, visual data-driven storytelling has been widely used. Yet, the vast majority of data communication occurs on desktop computers separated from the physical world the data originates in and refers to. Recent advances in Augmented Reality (AR) have shed new light on data-driven storytelling, offering exciting possibilities for telling engaging, in-situ, and immersive stories by embedding the data in the real-world context. However, although creating such kind of AR data stories is demanding and requires considerable knowledge and skills from different fields (e.g., data visualization, computer graphics, computer vision, and human-machine interaction), prior research has rarely investigated the effective way to create visual data-driven stories in AR environments. This thesis aims to fill this gap by exploring the approaches to facilitate the authoring of infographics, a popular format for data-driven storytelling, in AR. Given that AR devices are still evolving rapidly, this thesis focuses on the interaction between reality and virtuality, an essence of AR that is independent of specific devices. The first system, MARVisT, leverages physical properties (e.g., size, positions) from real-world objects to assist non-experts in creating 3D infographics in mobile AR. Given the limited interaction capabilities of mobile devices, in the second work, LassoNet is proposed to facilitate the selection of 3D objects in a 2D screen based on a deep neural network. Besides augmenting the physical world, the third research studies augmenting the semantic content of real-world infographics in AR and introduces PapARVis Designer to allow designers to create augmented static visualizations. Finally, the fourth work explores automating the creation of timeline infographics in AR by developing a deep-learning based method that automatically extracts extensible templates from real timeline infographics to generate virtual timelines with new data in AR. The core idea of this thesis is to allow visualization designers to go beyond the desktop platform to the engaging, immersive, and promising AR platform that is seen to be the next-generation human-machine interaction platform. The resulting systems and techniques blaze a trail toward futuristic visual storytelling.

Augmented reality sickness questionnaire (ARSQ): A refined questionnaire for augmented reality environment

Recupera algunos de los elementos menos estudiados y reconocidos en el ensayo latinoamericano: su profunda vocación de reflexión, diálogo y mediación intelectual; su plasticidad y apertura a la búsqueda del sentido; su vínculo con la sociabilidad, la conversación y la generación de un espacio simbólico de encuentro; su capa cidad de establecer una nueva retroalimentación entre lectura y escritura.

Augmented reality environments face significant challenges in achieving context and semantic awareness, which are relevant for enhancing user experiences. Although machine learning has been extensively applied to areas such as object and action recognition, there is a knowledge gap in the integration of these techniques to improve contextual understanding in AR environments. This study explores how machine learning enhances object and action recognition, improving context awareness in augmented reality environments. A systematic review of fifteen studies revealed advancements in deep learning, semantic web, knowledge graphs, and multimodal interfaces, which collectively improved the capabilities of augmented reality systems. The findings include innovations in aspects such as 3D hand pose estimation, semantic gaze analysis through eye tracking, and hybrid approaches that optimize real-time processing. These developments improve augmented and mixed reality environments by enabling more realistic, interactive, and responsive applications. However, persistent challenges such as latency, scalability, and environmental adaptation underscore the need for more research. These results suggest a guide for the development of highly immersive and efficient AR/MR systems, with the potential to improve user interaction and operational capabilities.

ABSTRACTPainting is a complex and creative process that involves the use of various drawing skills to create artworks. The concept of training artificial intelligence models to imitate this process is referred to as neural painting. To enable ordinary people to engage in the process of painting, we propose PainterAR, a novel interface that renders any paintings stroke‐by‐stroke in an immersive and realistic augmented reality (AR) environment. PainterAR is composed of two components: the neural painting model and the AR interface. Regarding the neural painting model, unlike previous models, we introduce the Kullback–Leibler divergence to replace the original Wasserstein distance existed in the baseline paint transformer model, which solves an important problem of encountering different scales of strokes (big or small) during painting. We then design an interactive AR interface, which allows users to upload an image and display the creation process of the neural painting model on the virtual drawing board. Experiments demonstrate that the paintings generated by our improved neural painting model are more realistic and vivid than previous neural painting models. The user study demonstrates that users prefer to control the painting process interactively in our AR environment.

The purpose of the research is to compare the impact of students’ learning performance between an augmented reality (AR) environment and in-class environment. To create an efficient AR environment, we used Microsoft HoloLens, which is the next generation of a see-through holographic computer. We developed the AR learning module: ergonomic guidelines for manual material handling (MMH). We hypothesize that AR changes the way students understand the concepts of MMH. Our analysis includes a careful evaluation of student experimental skills during the learning activities. This new AR environment could allow students to engage hands-on training of MMH and strengthen their understanding. Student test score was used as metrics for performance assessment. We found a significant improvement on student understanding of MMH lecture after they used the AR module. The findings of this study indicated the potential benefits of using AR environment interfaces in engineering education and training.

A truly anytime, anywhere approach to immersive analytics is predicated on the ability for users to author and explore visualizations in an immersive fashion and without a desktop computer. In this paper, we present a generalizable visual programming technique for immersive authoring, exploration, and presentation of data visualizations through direct manipulation, and implement it in a web-based augmented reality (AR) system. In a preregistered user study, we use this implementation to explore how analysts author and use data visualizations in immersive AR accessed using a head-mounted display (HMD). The study focuses on the authoring technique, spatial organization strategies, and sensemaking and presentation practices in such immersive analytics environments for HMD-based AR. Our findings suggest that participants particularly liked the direct manipulation of our approach, and that participants' spatial organization is not based on physical landmarks in the room.

AR/VR assisted integrated framework of autonomous disassembly system for industrial products

Background The modality and redundancy principles are well‐established within the cognitive theory of multimedia learning for their effectiveness in traditional media. However, previous studies applying these principles in virtual reality have shown contradictory results when immersive technologies are involved. Objectives This study investigates the effects of modality and redundancy principles from the cognitive theory of multimedia learning on cognitive load and learning outcomes in head‐mounted display augmented reality. Methods Using a between‐subjects experimental design, 104 male participants were randomly assigned to three conditions based on the way verbal instructions related to a T‐shirt folding procedure were presented (audio‐only, text‐only, text‐and‐audio). The presentation of the verbal instructions served as the manipulated variable; cognitive load levels and participants' performance were measured as outcome variables. Bayesian analyses were conducted to verify that there were no significant differences between the text‐and‐audio group and the audio‐only group, and a Partial Least Squares Structural Equation Model (PLS‐SEM) examined the relationship between the experimental condition, cognitive load levels, and participants' performance. Results and Conclusions Results support the validity of the modality principle in head‐mounted display augmented reality, showing that applying the modality principle significantly decreases intrinsic cognitive load and improves learning performances. The null hypothesis was supported by comparing audio‐only and text‐and‐audio conditions for both cognitive load and learning outcomes. These findings suggest that cognitive theory of multimedia learning principles work similarly in head‐mounted display augmented reality as in traditional media, and they offer valuable insights into multimedia learning in immersive augmented reality, extending our understanding of how these principles function in immersive technologies. Contradicting results using immersive technologies could be explained by considering task complexity and perceived intrinsic cognitive load; however, further studies testing different learning materials are necessary to support this hypothesis.

Virtual characters are now widely used in games, computer-generated (CG) movies, virtual reality (VR), and communication media. The continued technological innovations in motion capture mean that a more natural representation of a three-dimensional character’s motion should be achievable. Many researchers have investigated how virtual characters interact with their surrounding environment through spatial relationships, which were introduced for adapting and preserving character motion. However, technical problems should be resolved to enable the control of characters in augmented reality (AR) environments that combine with the real world, and this can be achieved by adapting motion to environmental differences using original motion datasets. In this paper, we investigate a novel method for preserving automatic motion adaptation for a virtual character in AR environments. We used specific object (e.g., puddle) recognition and the spatial properties of the user’s surrounding space, e.g., object types and positions, and ran validation experiments to provide accurate motion to improve the AR experience. Our experimental study showed positive results in terms of smooth motion in AR configurations. We also found that the participants using AR felt a greater sense of co-presence with the character through adapted motion.

Students often struggle to understand the vector dot product, which is a foundational operation used in mathematics and engineering. To improve undergraduate engineering students’ understanding of the dot product, we developed and tested the effects of an augmented reality (AR) app. The app utilized scaffolding and storyline narration to cover: (1) computation of the angle between vectors, and (2) the projection of a force vector onto a line. Students were randomly assigned to either a treatment group to utilize the AR, or a control group for traditional peer collaboration. Pre/post testing was conducted using a 14-item, 100-point test. 61 pairs of pre/posttest data (AR n = 25, control n = 36) were analyzed using ANCOVA. The 20.9-point improvement in the AR group's mean test scores was significantly larger than the 9.33-point increase in the control group. The effect size (partial η 2 = 0.135) was considered medium to large. The Instructional Materials Motivation Survey assessed motivation from 12 students in each group. Motivation of the AR group was 19.3% larger than that of the control. The difference was significant with a large effect size. The results suggest that the 3D visualization and immersive qualities of AR may improve learning of vector operations in STEM disciplines.

The immersive augmented reality (AR) system necessitates precise depth registration between virtual objects and the real scene. Prior studies have emphasized the efficacy of surface texture in providing depth cues to enhance depth perception across various media, including the real scene, virtual reality, and AR. However, these studies predominantly focus on black-and-white textures, leaving a gap in understanding the effectiveness of colored textures. To address this gap and further explore texture-related factors in AR, a series of experiments were conducted to investigate the effects of different texture cues on depth perception using the perceptual matching method. Findings indicate that the absolute depth error increases with decreasing contrast under black-and-white texture. Moreover, textures with higher color contrast also contribute to enhanced accuracy of depth judgments in AR. However, no significant effect of texture density on depth perception was observed. The findings serve as a theoretical reference for texture design in AR, aiding in the optimization of virtual-real registration processes.

Purpose: This study investigates the transformative role of augmented reality (AR) and virtual reality (VR) technologies in e-commerce and digital marketing. It aims to understand how these immersive technologies enhance consumer experiences by addressing challenges such as product visualization, consumer trust, and engagement while fostering higher purchase intent and customer satisfaction. Design/Method/Approach: A systematic literature review synthesizes findings from empirical and theoretical studies published between 2011 and 2024. The methodologies analyzed include experimental designs, market analyses, and conceptual frameworks assessing the integration of AR and VR technologies in online retail. Findings: The findings indicate that AR enhances online shopping through features such as virtual try-ons and spatial placement visualization, reducing return rates and increasing purchase confidence. VR, on the other hand, provides immersive environments that simulate physical shopping experiences, strengthening consumer-brand connections. While AR demonstrates broader applicability in fostering interactive and personalized experiences, VR remains limited by accessibility and cost constraints. Both technologies exhibit immediate costs but offer long-term benefits, positively influencing conversion rates and customer loyalty. Theoretical Implications: This study contributes to consumer behavior research by applying value perception models, immersive technology frameworks, and engagement metrics to the e-commerce landscape. It refines existing theoretical perspectives on technological adoption by highlighting the interplay between sensory engagement, emotional connection, and consumer decision-making. Practical Implications: The research underscores the strategic importance of integrating AR and VR into digital marketing and e-commerce platforms. It suggests leveraging AR for product categories requiring spatial interaction and using VR for high-detail product exploration. The study provides actionable insights for businesses looking to enhance online shopping experiences through immersive technologies. Originality/Value: This study offers a comprehensive synthesis of AR and VR applications in e-commerce, highlighting their potential to reshape online shopping. By bridging the gap between physical and digital retail experiences, it provides a fresh perspective on how immersive technologies can drive engagement, accessibility, and personalized consumer interactions. Research Limitations/Future Research: While this study synthesizes existing literature, empirical validation through longitudinal studies and cross-industry applications is necessary. Future research should explore the long-term effects of AR and VR adoption on consumer trust, loyalty, and behavioral changes. Additionally, further exploration is encouraged into gamification, artificial intelligence (AI) integration, and demographic-specific implementations to drive sustainable engagement.