Search Results (499)

The aim of this study is to explore the adoption of Smart Tourism (ST) and Fourth Industrial Revolution (4IR) skills demanded by the tourism industry, graduate preparedness, and curriculum alignment in accommodation establishments in KwaZulu-Natal, South Africa. A pragmatic explanatory sequential mixed-methods approach involved a quantitative survey of 295 respondents, semi-structured interviews with 11 industry managers, and curriculum document analysis. Qualitative data were thematically analysed, while quantitative data were analysed using descriptive statistics, reliability analysis, and exploratory factor analysis. The results show a high uptake of ST technologies and demand for digital competencies. The competencies most required by employers were digital marketing and social media (M = 4.13), mobile application use (M = 4.07), and basic computer literacy (M = 4.03). However, graduates were viewed as less proficient in AI (M = 2.69), cybersecurity (M = 2.67), virtual and augmented reality (M = 2.60), and blockchain technology (M = 2.55). Reliability analysis revealed good internal consistency (Cronbach’s α = 0.742–0.950). The integrated findings indicated a clear gap between industry expectations and curriculum provision, leading to the development of a proposed evidence-informed Transformative Curriculum Model (TCM) to enhance graduate employability and support ST implementation. Full article

This paper aims to evaluate the usability and user experience of ad hoc augmented reality systems in the learning experience of mechanical engineering students, focusing on fluid machinery education. Three mobile augmented reality applications were developed to support teachers in explaining the main parts of an impeller blade and its fluid interaction, integrating computer-aided design (CAD) models with velocity and pressure maps derived from computational fluid dynamics (CFD) simulations. By providing multiple means of representation, these tools were developed to support the explanation of complex 2D concepts without requiring specialized hardware. The obtained results revealed that the usability of the developed applications, assessed through the System Usability Scale (SUS), was remarkably effective. Furthermore, the User Experience Questionnaire (UEQ) showed that the average scores for each evaluation criterion were highly positive, especially in the “Stimulation” and “Novelty” areas. Accurate statistical analyses revealed that students’ feedback was not influenced by users’ familiarity with virtual and augmented reality tools. In conclusion, since no objective learning gains were evaluated, this investigation’s outcomes indicate that the developed applications provide an engaging tool with high usability and positive user experience, framing inclusive education as a fundamental design rationale rather than an empirically demonstrated outcome and laying the groundwork for future studies to objectively measure cognitive impact. Full article

Augmented reality (AR) enhances the real world scene by overlaying virtual information onto it. Vision-based 3D tracking and registration is the key technology for ensuring the fusion of virtual and real content in monocular AR systems. Existing mainstream visual tracking and registration methods are susceptible to illumination variations, motion blur, target occlusion, and dynamic background interference in complex scenarios. They also suffer from low computational efficiency, cumulative pose errors, and insufficient stability, making them difficult to deploy on low power edge devices such as embedded systems and mobile terminals. To address these issues, this paper proposes a lightweight monocular AR 3D tracking and registration method that integrates ORB-FREAK features, mismatching outlier filtering, background weighted mean shift, and template-based relocalization. The method does not rely on depth sensors or neural network inference, enabling efficient and accurate lightweight pose estimation. Specifically, we first combine the ORB (Oriented FAST and Rotated BRIEF) descriptor with the FREAK (Fast Retina Keypoint) algorithm for feature detection and initial matching. Hamming distance is used for coarse filtering of mismatched point pairs, and an ascending sort combined with an iterative sequential sampling strategy is applied to solve the optimal homography matrix, significantly improving the accuracy and efficiency of matrix estimation. Then, distance constraints among feature points are imposed on the target registration region to optimize the selection, and camera pose is computed based on the matching between 2D feature points and their corresponding 3D spatial coordinates, eliminating the error accumulation problem of conventional algorithms. Real-time feature matching is further used to correct the optical flow tracking sequence and camera pose, ensuring the continuity of the AR tracking process. Finally, a background weighted mean shift algorithm is introduced to narrow the feature detection range and suppress background interference, complemented by a template-matching relocalization module and a dynamic model update strategy, which effectively enhance the robustness of continuous tracking and registration under complex conditions. Experimental results demonstrate that, in extreme scenarios such as low light conditions, high speed motion, and occlusion, the proposed method achieves AR 3D tracking and registration success rates of 86.7%, 82.3%, and 78.5%, respectively. It exhibits superior performance in pose estimation accuracy and anti-interference capability in complex environments, with significantly reduced computational overhead. Moreover, it can achieve robust and continuous AR 3D tracking and registration on low power edge devices, effectively adapting to demanding AR application scenarios and providing reliable technical support for lightweight AR applications. Full article

STEAM education integrates science, technology, engineering, arts, and mathematics to foster interdisciplinary learning and support the development of 21st-century skills. However, implementing this approach requires the development of educational resources that meaningfully connect these disciplines. Augmented Reality (AR) can contribute to this effort by enabling interactive and immersive learning experiences. This paper presents AR-GenSTEAM, a generator for creating STEAM-based serious games with AR for web and mobile platforms. The generator follows a three-stage development process comprising analysis, configuration, and generation. Through this process, users select STEAM areas, target skills, game templates, AR resources, and an export platform to generate a serious game. A case study of Enkrypto, a serious game focused on encryption and decryption, is presented to illustrate the use of AR-GenSTEAM. A hybrid evaluation was conducted to evaluate the performance of AR-GenSTEAM and students’ experiences with the AR component of the Enkrypto game. The generator successfully generated web applications in 90% of attempts and mobile applications in 80%, while students rated the AR component positively in terms of pragmatic quality, hedonic quality, and overall user experience. Full article

The growth of cloud services and immersive applications based on extended reality (XR), including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), imposes increasingly demanding requirements on access networks. The large-scale integration of IoT devices in smart buildings further increases the need for high throughput, low latency and jitter, and reliable connectivity. Traditional Fiber-to-the-Home (FTTH) networks with a single access point (AP) become quite limiting when there are high performance requirements, with many users with indoor mobility and high device density. Fiber-to-the-Room (FTTR) is an extension of FTTH, which brings fiber optics to each room of the house through a Main FTTR Unit (MFU) and several Sub FTTR Units (SFU) along with the APs, with centralized device management. Green FTTR networks are characterized by their energy efficiency through centralized control of signal power and Dynamic Bandwidth Allocation (DBA) management. The fgONT architecture allows for deterministic network slicing, enabling the allocation of specific resources isolated from the rest of the network traffic, allowing for predictable bandwidth and QoS. This work presents a framework that allows for a comparative analysis of FTTR and FTTH networks in different scenarios in order to ensure a compromise between transmission quality, network energy efficiency, and the user’s perceived experience. The results obtained show that, in high device density scenarios, FTTR reduces the average packet loss from 52.69% to less than 0.08%, decreases the average latency from 151 ms to less than 2 ms, and maintains the overall QoE above 0.974, compared to 0.27 in FTTH with a single AP. Full article

Integrated qiaopi office–residence heritage preserves the material setting of remittance-letter operations together with domestic, educational, and ritual activities. In Lingnan’s hot–humid and disaster-prone environment, condition records need to be repeatable, spatially traceable, and continuously updatable. Taking Jingzu Jiashu and Mingde Jiashu, two former qiaopi office sites in Chaoshan, as case studies, this research develops an evidence-traceable digital conservation workflow integrating multi-source documentation; an adopted YOLOv8 surface-deterioration baseline; qualitative Grad-CAM visualization; structured deterioration records; and semi-automatic, human-confirmed Revit/HBIM association. UAV and terrestrial photography, mobile LiDAR/scanning, handheld measurement, measured drawings, point-cloud and reality-based products, and geometric models were organized into case-specific HBIM environments. The adopted deterioration dataset comprised 362 original images at 512 × 512 pixels and 2024 bounding-box annotations for five visually identifiable categories: spalling, staining, plants, saltpetering, and crack. The original images were divided into 253 training, 72 validation, and 37 independent-test images, while augmentation was restricted to the training subset, increasing the training pool to 1600 images. The previously established YOLOv8 baseline achieved a Precision of 0.85, Recall of 0.72, mAP50 of 0.83, and mAP50–95 of 0.58. Grad-CAM heatmaps were used as qualitative aids to examine model-emphasized image regions. Retained detections associated with Jingzu Jiashu and Mingde Jiashu were converted into versioned records containing source-image identifiers, deterioration classes, detector confidence, survey information, spatial references, verification states, and revision histories. Candidate spatial associations were generated through case identifiers, façade or space zones, element identifiers, and available spatial evidence, while final M1–M3 associations required human confirmation. By preserving source provenance, spatial uncertainty, and record histories, the workflow provides an auditable information basis for routine inspection, post-event review, maintenance prioritization, repair interpretation, and resilience-oriented preventive conservation. The workflow supports screening-level deterioration recognition and information management but does not provide causal diagnosis, structural assessment, exact affected-area measurement, building-independent generalization, or automatic repair recommendations. Full article

On-site digital human guide systems, which integrate intelligent interactive technologies with guidance, interpretation, and information services, are emerging as an important form of intelligent on-site service. However, existing knowledge remains largely fragmented across technological configurations, interaction modalities, application contexts, and user experience objectives, lacking an integrated cross-dimensional analytical perspective that explains how these systems create and deliver value. To address this gap, this paper aims to adopt a Product–Service Systems (PSS) perspective to systematically examine the development of on-site digital human guide systems. Specifically, it explores their product–service configurations, service delivery modes and user participation, and artificial intelligence (AI) capability integration pathways, while identifying the value creation opportunities and challenges associated with system development and continuous optimization. The PSS paradigm offers such an integrative lens, which has gained renewed relevance as the Fourth Industrial Revolution (Industry 4.0) accelerates the servitization and digital transformation of traditional services. This paper conducts a scoping review employing content analysis based on a structured cross-database search across Google Scholar, Scopus, and Web of Science, supplemented by snowball sampling, covering 40 studies, including 34 deployed projects. The findings reveal that: (1) on-site digital human guide systems can be categorized into four product–service configurations: fixed terminal (stationary dialogue); mobile terminal (location-aware guidance); Head-Mounted Display/Mixed Reality (immersive experience); and robot (full-process mobile service). The selection of these configurations is shaped by spatial characteristics, target user group, and institutional operational conditions; (2) a user behavior taxonomy of nine active and five passive types was developed, demonstrating that user participation patterns are jointly shaped by control allocation in service delivery and the social behavior design of digital humans, with users showing a preference for controllable and interruptible engagement; (3) AI integration has evolved from rule-and-script-driven approaches to modular AI integration, and subsequently to large language model (LLM)/Retrieval-Augmented Generation (RAG)-driven architectures, with corresponding design implications proposed; and (4) challenges were classified into four major categories and 26 subcategories across three digital transformation stages. The contribution of this paper lies in the development of an integrated PSS analytical framework for on-site digital human guide systems, spanning product, service, and AI integration layers. The framework provides a multi-layer analytical lens for understanding system configurations, user participation, technological evolution, and implementation challenges, while offering structured guidance for configuration and service selection, system design, and continuous optimization across diverse deployment contexts. These findings provide practical implications for researchers and practitioners seeking to design, deploy, and optimize on-site digital human guide systems across diverse service environments. Full article

The rapid growth of computing-intensive and latency-sensitive applications, including augmented reality, virtual reality, and self-driving systems, has increased the demand for low-latency and energy-efficient processing solutions. Mobile edge computing (MEC) has evolved as a transformative paradigm by relocating computation to the network edge, closer to end users. Unmanned aerial vehicles (UAVs) further strengthen MEC by offering flexible deployment, mobility, and reliable line-of-sight communication, making them suitable for temporary high-demand scenarios. Moreover, such latency-sensitive applications often generate numerous repetitive tasks and, thus, storing the results of these tasks can reduce both communication overhead and computational workload. However, jointly addressing the caching of task-results alongside offloading and resource allocation decisions in UAV-aided MEC networks remains a non-trivial challenge. In this study, an integrated task offloading and resource allocation with data caching (JORC) framework is proposed to address these challenges. The offloading and resource allocation problems are formulated as a Markov decision process and solved using the soft actor–critic reinforcement learning algorithm. In addition, dynamic and adaptive caching manages limited storage and reduces redundant computations by using a hybrid strategy that integrates the least-frequently used and least-recently used policies to reduce computational redundancy. Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches. Full article

Quality management in mega construction projects is increasingly supported by digital technologies, yet quality information often remains fragmented across inspection systems, reality-capture platforms, Building Information Modelling (BIM) environments and enterprise systems. This fragmentation limits traceability, delays corrective actions and weakens the connection between quality performance, contractual obligations and financial accountability. This study develops a closed-loop digital QA/QC framework that integrates BIM, mobile field inspection, reality capture, artificial intelligence (AI) augmentation and enterprise resource planning (ERP) within a unified governance architecture. Following a Design Science Research approach, the study proposes a seven-layer framework linking quality events to procurement, financial control, and project-management processes while supporting role-based decision-making through data democratisation mechanisms. The framework extends conventional ERP-enabled quality management by explicitly incorporating procurement (MM) and financial-control (FI/CO) functions, including supplier-quality management, cost-of-poor-quality tracking and quality-linked payment governance. An illustrative project scenario and sensitivity analysis are used to demonstrate the application of the proposed KPI and evaluation structure. By treating integration as the primary design objective, the framework provides a foundation for enterprise-wide digital quality management, lifecycle information continuity and digital-twin readiness in mega construction projects. The contribution itself is evaluated through an illustrative Design Science Research demonstration and an assumption-bounded sensitivity analysis rather than through field data, with future empirical validation specified through a controlled before-and-after case-study protocol. Full article

Effective evaluation of building fabric is essential to understanding building performance. Traditional methods, such as ISO 9869-1, provide point U-values but do not always capture the complete picture, and new measurement-led approaches are needed to support building retrofit. Heat3D is a novel iOS application that performs rapid U-value measurements of building elements by mapping thermographic images from a mobile infrared camera onto an augmented reality (AR) model of a room, from which heat flux across the element is calculated. A field trial of 22 UK properties during the 2019–2020 winter heating season assessed Heat3D’s heat flux measurements against traditional heat flux plates, with 90% of 295 Heat3D surveys falling within the combined uncertainty of the reference method. A second field trial, conducted over the 2020–2021 winter heating period, evaluated a new timelapse iteration of the method capable of measuring wall U-value within approximately 60 min; 90% of 42 Heat3D surveys fell within the combined confidence interval of the ISO 9869-1-measured U-value. These results indicate that Heat3D offers a viable, rapid alternative to traditional methods for both heat flux and U-value measurement. Full article

Background: Modern household appliances often present complex interfaces that can be difficult to use, especially for older adults, people with visual impairments, and users with mild cognitive difficulties. In such cases, interacting with appliances may require sustained attention, visuospatial search, working memory, and sequential action planning, while traditional user manuals often provide limited contextual support. Methods: To address this issue, this study presents a proof-of-concept augmented reality (AR) and artificial intelligence (AI)-based system for contextual appliance guidance. The proposed architecture integrates visual sensing, deep learning, and large language models to detect appliance controls, interpret user queries, retrieve relevant information from user manuals, and provide step-by-step guidance directly on the real interface. A YOLOv8 model trained on a custom dataset was used for button detection, YOLO-Seg was employed to enhance visual highlighting through segmentation, and BoT-SORT was used to maintain detection consistency across frames. A Unity-based mobile application displayed real-time AR overlays with customizable visual settings for accessibility needs, such as low vision and color blindness that may be relevant for future accessibility-oriented applications. In addition to its technical pipeline, the system is conceptually relevant as a potential form of external cognitive support because it transforms static manual instructions into situated, sequential, and visually grounded guidance.Results: Experimental results showed promising technical performance for button detection and segmentation, while a preliminary user evaluation in a non-clinical sample suggested good usability, clarity, and acceptability of the interface. Conclusions: These findings support the technical feasibility and preliminary usability of the approach, while cognitive workload, confidence, functional autonomy, and clinical benefit were not directly measured. Targeted validation in older adults, people with visual impairments, and clinical populations is therefore still needed. Future developments will include multimodal feedback, read-aloud guidance, and more specific evaluation of workload, confidence, and functional autonomy. Full article

Japan is one of the most earthquake-prone countries in the world. The 2011 Great East Japan Earthquake alone caused nearly 20,000 deaths and missing persons. International visitors and newly arrived foreign residents in Japan may face heightened risk during earthquakes, as limited prior experience with earthquakes and limited Japanese-language proficiency can restrict access to essential safety information. In addition, because disaster-preparedness education is often insufficient in countries with low disaster risk, they may be especially vulnerable in emergencies. This study designed, developed, and evaluated an augmented reality (AR) application intended to provide earthquake-preparedness and survival guidance in the Japanese context. The application was evaluated using a pre-test/post-test design with 40 participants from 10 countries, who completed a 14-item multiple-choice disaster-preparedness knowledge test and a user satisfaction survey. Participants’ knowledge scores improved significantly from the pre-test to the post-test (t(39) = 8.20, p < 0.001), with a large effect size (Cohen’s d = 1.30), and participants reported high satisfaction (4.55/5, SD = 0.61). These results suggest the potential of AR as an accessible educational approach for enhancing earthquake-preparedness knowledge among international visitors and newly arrived foreign residents in earthquake-prone regions. Full article

Egocentric multi-view image analysis refers to the processing of utilizing synchronized video streams captured from multiple wearable cameras worn on the head or body, providing complementary first-person perspectives of dynamic, real-world interactions. Unlike single-view egocentric vision, which may suffer from severe occlusions, motion blur, and limited field-of-view or traditional fixed-camera multi-view setups (assuming static geometry and controlled environments), egocentric multi-view systems leverage body-worn rigs to enable a more robust and flexible 3D understanding in open-world, mobile scenarios. In this work, we present a systematic survey of advancements in cross-view feature fusion, geometric consistency enforcement, open-world detection, human–object interaction (HOI) modeling, action segmentation, 3D reconstruction, and novel-view synthesis specifically tailored to wearable multi-camera platforms. Key datasets released between 2024 and 2026—including HOT3D (833 min of synchronized multi-view hand/object interactions from Project Aria and Quest 3), MultiEgo (first multi-egocentric dataset for 4D social scene reconstruction), and Ego-1K (large-scale 12-camera rig for dynamic 3D video synthesis) are thoroughly examined alongside an analysis of integrations with large language models (LLMs) and vision–language models that drive performance gains, typically in the 15–30% range over single-view baselines in hand tracking, HOI recognition, and reconstruction fidelity, although we show through a consolidated meta-analysis that this gain is task-dependent: larger for geometry-bottlenecked tasks such as in-hand object lifting, and smaller, method-dependent, or occasionally negative for semantic-recognition tasks such as keystep recognition under naive view fusion. These methods cover work in multi-view stereo, cross-view learning, and novel-view synthesis while addressing several real-time wearable constraints. Practical applications such as immersive Augmented Reality/Virtual Reality (AR/VR), assistive robotics, and healthcare monitoring are also discussed together with the challenges in motion calibration, benchmark diversity, and edge deployment ability. Thus, in this review, we attempt to fill a critical gap by focusing exclusively on wearable multi-view systems in an open-world setting, synthesizing the latest literature to chart future directions toward more embodied and continual learning agents. Full article

Immersive teleoperation of quadruped robots requires the operator to interpret a remote environment, make decisions, and maintain control over a dynamic platform through mediated visual feedback and networked command transmission. This study presents and validates a reproducible augmented reality (AR)–ROS 2 architecture designed to analyze the relationship between system-level technical conditions and operator experience during immersive teleoperation of a physical quadruped robot. The system integrates Meta Quest 3, Unity, UDP communication, ROS 2 middleware, and Xiaomi CyberDog within a modular workflow that jointly supports immersive control, live RGB/depth perception, physical robot execution, and user-centered HRI evaluation. The architecture decouples command transmission and visual feedback into two UDP-based channels: the control channel maps virtual joysticks and discrete HUD buttons to ROS 2 locomotion and action commands, while the perception channel transmits compressed RGB and depth frames for display in the AR interface. Under nominal conditions, the control channel achieved a mean end-to-end latency of 9.37 ms, a P95 of 18.45 ms, an effective update frequency close to 18 Hz, and 100% packet reception. Compared with ROS-TCP-Endpoint, the proposed UDP bridge showed similar latency but higher flow integrity, avoiding duplicate, parsing, and dropped-command events. The visual channel achieved mean end-to-end latencies of 21.09 ms for RGB and 26.99 ms for depth, with frame reception rates of 91.78% and 91.10%, respectively, under a shared 18.67 Mbps mobile network. A complementary network degradation analysis showed that the control channel remained operational under bandwidth reduction, packet loss, added latency, combined degradation, and physical separation of the robot. The user evaluation with 25 participants yielded a corrected Raw NASA-TLX score of $7.13\pm 4.10$, a positive perceived performance score of $95.20\pm 5.10$, and a System Usability Scale score of $90.30\pm 6.63$, indicating low perceived workload and high usability. These results show that low end-to-end latency, high flow integrity, and stable visual feedback were accompanied by low perceived workload and high usability, indicating that the technical temporal response, stability, and robustness of the communication channels are consistent with an effective and low-demand operator experience during AR-based teleoperation of a physical quadruped robot. Full article

In multimodal learning interfaces, effective learning support depends not merely on adding text, images, audio, or spatial interaction, but on whether knowledge representations can be understood, navigated, and transformed into cognitively manageable learning support across platforms. In vocabulary learning, fragmented lexical information may create visual load, operational interference, or process misalignment when representational structures and platform conditions are not appropriately coordinated. This exploratory mixed-methods study examined how representational structure, platform affordance, and learning process align in three concept-mapping interface configurations: APPHC, APPRC, and ARRC. Forty-five adult participants completed pre-test, immediate post-test, and delayed post-test measures, an attitude scale, and semi-structured interviews. After controlling for pre-test differences, no significant group superiority was found in immediate learning, delayed retention, or attitudes, indicating that no single configuration could be interpreted as generally superior. Qualitative findings nevertheless revealed different learning processes behind similar quantitative outcomes: APPHC supported semantic organization and stable review, APPRC encouraged associative exploration but required clearer visual guidance, and ARRC enhanced immersion and embodied experience while increasing interaction and cognitive-regulation demands. Based on these findings, the Platform–Structure–Process (PSP) Model is proposed as a preliminary design-diagnostic framework for the early design and evaluation of multimodal concept-mapping interfaces. Full article