Search Results (610)

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

Traditional evaluations of artificial intelligence (AI) systems in the dynamic, operator-dependent, and time-sensitive field of interventional radiology (IR), focusing solely on algorithmic performance, often fail to capture their real-world clinical impact. This narrative review aims to provide an overview of the current state of the art of AI integration in IR through human–AI interaction (HAI), while offering a critical perspective on their clinical integration, limitations, and future directions. A comprehensive survey of recent literature was performed, focusing on AI applications across procedural phases. The review emphasizes systems providing decision support, real-time procedural verification, and immersive interfaces (augmented and virtual reality), while critically evaluating determinants of effective clinical adoption. AI has shown preliminary potential to support operator performance in selected interventional radiology tasks, although most applications remain experimental, retrospective, or evaluated in phantom or preclinical settings. Potential benefits include structuring uncertainty in patient selection and procedural planning, supporting assessment of device positioning and treatment outcomes, and integrating AI-derived outputs into the operator’s spatial field through immersive technologies. The clinical utility of these systems appears to be influenced by human–AI interaction, with interpretability, workflow integration, and trust calibration representing key determinants of effective use beyond algorithmic accuracy alone. The potential value of AI in interventional radiology appears to derive from its integration into human decision-making rather than from standalone predictive performance alone. A human-centered, interaction-based model supports understanding current applications, address challenges, and guide the development of adaptive, real-time systems for dynamic procedural environments. Full article

Higher education is undergoing a transition in which static multimodal resources are giving way to immersive learning environments powered by generative artificial intelligence (GenAI). This PRISMA 2020-compliant systematic review, prospectively registered in INPLASY (202610066), synthesizes evidence on immersive GenAI-based strategies in higher education, examining their reported contributions to sustainability, inclusion, and learning outcomes. Searches across Scopus, ScienceDirect, and ERIC (2022–2026) identified 1364 records; after quality appraisal using an adapted CASP instrument, 25 studies were included in a narrative and descriptive synthesis. Five strategy types emerged—VR-based simulations, virtual patient platforms, adaptive LLM tutoring systems, mixed/augmented reality environments, and 3D/metaverse configurations—with GPT-family models predominating (56%). The central finding is a structural reporting asymmetry: learning outcomes were explicitly documented in 23 studies (92%), whereas sustainability and inclusion were explicitly reported as outcome domains in only one study each (4%). Health sciences (36%) and educational technology (28%) dominated the evidence base, while Latin American, African, and most STEM contexts remained underrepresented. Immersive GenAI strategies are being evaluated for short-term instructional value, while their contribution to sustainable higher education remains underexamined. Advancing SDG 4 requires longitudinal designs, equity-oriented frameworks, and indicators capable of evaluating inclusion and durable learning gains across institutional contexts. Full article

Pose estimation is an important task in the field of machine vision, being widely used in robot grasping, augmented reality, and other applications. Weakly textured objects pose severe challenges due to scarce texture and low-density features, becoming a bottleneck in robot grasping. This paper systematically reviews recent progress in weakly textured object pose estimation, classifying methods into traditional and deep learning categories, and further dividing deep learning methods into instance-level, category-level, and unseen object-level. This review further summarizes the core issues of generalization limitations, real-time contradictions, and data bottlenecks in existing research. Combined with the practical needs of weakly textured scenes, the review points out that multimodal fusion optimization, lightweight model design, and low-cost annotation technology development are the future core research directions. The research results can provide a reference for algorithm design, experimental verification, and engineering applications in the field of weakly textured object pose estimation. Full article

Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints. Full article

Extended Reality (XR), including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is increasingly used to support experiential learning in Initial Teacher Education (ITE). This systematic review aimed to examine how XR technologies are integrated into university-based ITE programmes and their reported educational outcomes. Following PRISMA 2020 guidelines, a multi-source search was conducted across major databases (e.g., Scopus, Web of Science) and the grey literature (last search: January 2026). Eligible studies included empirical research on XR in ITE published between 2016 and 2026; non-empirical and non-ITE studies were excluded. Risk of bias was assessed using established appraisal criteria, and results were synthesised using a narrative thematic approach. A total of 32 studies were included. Findings indicate that XR is primarily used for classroom management training, microteaching, and reflective practice. Across studies, immersive simulations were associated with improvements in teacher self-efficacy, classroom management skills, and reflective decision-making. However, accessibility and inclusion strategies remain underdeveloped, and evidence of transfer to real classroom practice is still limited. Overall, XR functions most effectively as a preparatory tool that complements practicum-based training. Full article

Healthcare professionals must acquire and maintain both declarative knowledge and fine psychomotor skills across a wide range of clinical procedures. Human working memory is physiologically limited, and the high cognitive demands of clinical environments frequently contribute to medical errors and adverse events. Intra-individual performance variability—driven by fatigue, stress, and motivation—represents a further challenge that conventional medical safety education has not adequately addressed. According to the World Health Organization, patient harm ranks fourteenth in the global burden of disease, with approximately 10% of hospitalised patients in high-income countries experiencing harm within healthcare facilities. This study reports the design, theoretical rationale, and preliminary outcomes of an augmented reality (AR) glasses system for hands-free, self-directed medical procedural training, developed from a human factors and ergonomics (HFE) perspective. The system integrates a see-through head-mounted display (HMD; Epson Moverio BT-40S), bone-conduction earphones (Shokz OpenComm), and an industrial-grade voice recognition application (NEC Solution Innovators), achieving fully hands-free operation compatible with aseptic technique. Content design is grounded in cognitive load theory (CLT) and the cognitive theory of multimedia learning (CTML), extended by neuroscientific evidence on multisensory integration and memory consolidation. More than 40 procedure-specific modules have been developed in-house at Tokyo University of Technology, spanning airway management, vascular access, respiratory therapy, dialysis, and cardiac support. In a four-year longitudinal survey (virtual reality (VR) simulator; n = 286), major satisfaction items consistently exceeded the scale midpoint. In an AR endotracheal suctioning cohort (n = 38/22), procedural flow understanding was rated 3.95/5.0. A peer-reviewed randomised controlled trial (Clinical Simulation in Nursing, n = 36) demonstrated significantly superior skill improvement (p < 0.001) and learning motivation (p = 0.001) in the AR group versus textbook self-practice. Principal ergonomic limitations of current HMD hardware—excessive weight, narrow field of view, and absence of medical-grade certification—are documented, and AI-based real-time procedural assessment is identified as a priority for the next research phase. Full article

The field of pose estimation is a major problem in computer vision, enabling the direct transformation of an input image into a hierarchical representation of the human skeleton for application in the fields of virtual/augmented reality and human–machine interaction tasks. Research in this field has exploded between 2018 and 2025, with traditional taxonomies such as 2D versus 3D or top-down versus bottom-up no longer sufficient to capture the essence of the evolution of ideas. To solve this problem, we propose a conceptual review in the field of pose estimation, focusing on the intellectual evolution of methods and architecture rather than the standard flat classifications of papers. We divide recent advances into five structural pillars: Representation, which traces the evolution from pixel coordinate regression to heatmaps and probabilistic representation; Architecture, which analyzes the transition from multi-stage CNNs to transformers and state space models (SSMs); Ambiguity and Generalization, which analyzes how self-supervised, uncertainty-aware, and diffusion models address 3D depth ambiguity, occlusion, and domain gaps by modeling multiple plausible poses and reducing dependence on fully supervised in-the-wild 3D labels; Context Extension, which covers temporal dynamics, multi-view fusion, and potential sensors; and Applications, which links algorithms to efficiency, privacy, and foundation models. By providing an in-depth detailing of these pillars, we provide a unified view of the evolution of research paradigms that define human pose estimation and enable the identification of future problems and solutions in pose estimation and human-centered tasks. Full article

Introduction: Neurosurgery has evolved from an anatomy-driven analog discipline into a digitally augmented field supported by multimodal imaging, neuronavigation, intraoperative imaging, neurophysiological monitoring, robotics, augmented reality, and artificial intelligence. Objective: To examine how this transition has altered professional responsibility, informed consent, training, and medico-legal accountability in neurosurgical practice. Methods: We performed a structured narrative review of the literature on digital neurosurgery and its ethical and professional implications, focusing on publications from 1990 onward and supplemented by landmark historical papers. Sources were selected for relevance to cranial, spinal, skull base, stereotactic, and neuro-oncological neurosurgery, and then synthesized into thematic domains including brain shift, eloquent cortex preservation, stereotactic accuracy, intraoperative neurophysiology, workflow integration, equity, and liability. Results: Digital systems improve lesion localization, function-preserving surgery, stereotactic precision, documentation, and training, but they also introduce new vulnerabilities related to registration error, brain shift, platform dependence, data overload, cost, cybersecurity, deskilling, and diffuse accountability. Conclusions: Digital augmentation expands rather than diminishes the neurosurgeon’s responsibility. The neurosurgeon remains accountable for surgical indication, interpretation of technology-generated information, intraoperative override, and communication of technology-specific risks. The central ethical challenge is to integrate digital tools without weakening patient-centered judgment. Full article

Transitioning toward efficient and digital industrial design requires preliminary tools that support early decision-making in plant layout studies. This qualitative and exploratory study analyzes an Artificial Intelligence (AI)-assisted and Augmented Reality (AR)-supported workflow within the Systematic Layout Planning (SLP) framework for the preliminary spatial evaluation of a cosmetic emulsion production plant. The study was developed as a case study based on a previously reported layout for obtaining cosmetic emulsions from Amazonian oils. A top-view layout was examined through structured prompts aligned with SLP criteria, including product journey, activity relationships, relational diagrams, and space requirements. ChatGPT was used only as a qualitative reasoning assistant, without optimization, prediction, mathematical modeling, or algorithmic functions. After the AI-assisted review, the refined layout was represented in three dimensions and visualized through AR in a real environment. The results identified potential improvements related to operational flow, traceability, critical area relationships, and spatial organization. AR-assisted visualization provided preliminary visual evidence of compatibility between the refined layout and the selected site, supporting an early review of circulation, access, and volumetric behavior. The sequential integration of SLP, AI, and AR is proposed as an exploratory workflow for early-stage layout evaluation, pending future quantitative validation studies and expert technical review. Full article

In safety-critical domains such as aviation, autonomous driving, and defense, operators must process complex spatial and radar data under severe time pressure. Traditional two-dimensional interfaces often force a “head-down” posture, increasing cognitive workload and impairing situational awareness. Extended reality and multimodal interfaces—incorporating gesture, voice, and haptic feedback—offer a promising paradigm to mitigate these limitations by enabling natural, egocentric data visualization. This scoping review systematically maps the empirical evidence on immersive and multimodal interfaces designed for radar and spatial data visualization in critical operational environments. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines, a systematic search was conducted across five major databases for articles published between 2015 and 2025. Out of 538 unique records screened, 54 studies met the eligibility criteria and underwent structured data charting. The findings reveal a technological ecosystem heavily dominated by augmented reality and virtual reality, supplemented by non-extended reality multimodal baselines (n = 8) to evaluate sensory load distribution. While subjective metrics such as the NASA Task Load Index (n = 17, 31.4%) dominate current evaluation practices, there is a notable scarcity of objective real-time physiological biosensors (n = 7, 13%). Crucially, the synthesized data challenges uncritical technological optimism: while multimodal extended reality effectively mitigates visual bottlenecks, certain modalities like mid-air gestures frequently induce physical fatigue and a documented speed–accuracy trade-off. To fully realize the potential of immersive decision support systems, future research must prioritize standardized, ecologically valid evaluation frameworks and explore artificial intelligence-driven adaptive interfaces capable of dynamically modulating information density based on operator workload. Full article

This study is set against a backdrop of interest in understanding how secondary school pupils perceive learning experiences based on Augmented Reality (AR) and digital assessment, with the aim of designing a useful tool for comparative and replicable research in AR-STEM contexts. To this end, an attitudinal questionnaire was developed and preliminarily validated through a quantitative study conducted with a sample of 199 students from various schools who worked on science curriculum content using an AR application created and validated for the explanation of key concepts. The instrument, designed ad hoc based on the TAM and IMMS models and peer-reviewed, comprised 35 items. Its reliability and construct validity were analysed using Cronbach’s alpha and exploratory and confirmatory factor analysis. The results showed favourable scores for motivation, technological acceptance and the evaluation of digital assessment, alongside high internal consistency and a stable three-factor structure. In conclusion, the instrument presented in full in this study provides preliminary evidence that it is a valid, reliable and useful tool, although the digital assessment dimension remains emerging and will require further validation with independent samples. Full article

Background/Objectives: Transsphenoidal surgery has become the gold standard for the treatment of sellar and parasellar lesions, but it remains associated with significant anatomical challenges and the risk of intraoperative complications. The limitations of conventional imaging in depicting the complex three-dimensional anatomy of the skull base have led to a growing interest in virtual (VR) and augmented reality (AR) technologies, which offer enhanced spatial visualization, preoperative simulation, and image-guided intraoperative navigation. This systematic review aims to evaluate the current evidence on the role of virtual and augmented reality in transsphenoidal surgical interventions, with a focus on their impact on preoperative planning, intraoperative orientation, surgical outcomes, and neurosurgical training. Methods: A systematic literature search was conducted in accordance with PRISMA 2020 guidelines across PubMed, Scopus, and Web of Science for the period 2015–2025. MeSH terms and free-text keywords related to transsphenoidal surgery, sphenoid sinus anatomy, and VR/AR technologies were combined using Boolean operators. Risk of bias was assessed using RoB 2.0 for RCTs; methodological quality was assessed using the Newcastle–Ottawa Scale for observational studies and AMSTAR 2 for systematic reviews. Clinical, morphometric, and experimental studies evaluating VR/AR applications were included. Data were extracted using a standardized protocol and synthesized through qualitative analysis, with subgroup analysis by technology type (VR vs. AR) and clinical application domain. Results: A total of 218 publications were identified, of which 52 met the inclusion criteria (clinical studies n = 12, simulation and technology studies n = 30, morphological studies n = 10). VR-based three-dimensional reconstructions were consistently associated with improved preoperative spatial orientation and anatomical landmark recognition. AR systems demonstrated a meaningful contribution to intraoperative navigation, with reported reductions in time to target and improved visualization of critical neurovascular structures. VR platforms showed high effectiveness in surgical training, with shorter learning curves and improved technical performance. However, the majority of included studies were small observational cohorts, simulation studies, or expert overviews, with substantial heterogeneity in methodology, technology platforms, and outcome measures, precluding quantitative meta-analysis. Conclusions: Virtual and augmented reality represent clinically promising adjuncts to transsphenoidal surgery, with demonstrated benefits in preoperative planning, intraoperative navigation, and surgical training. These conclusions should be interpreted in the context of a predominantly early-phase and heterogeneous evidence base. Standardized protocols, larger prospective studies, and randomized trials are needed before the integration of VR/AR with navigation systems and artificial intelligence can be established as a routine component of personalized transsphenoidal surgery. Full article

Adrenal tumors are increasingly diagnosed due to widespread use of cross-sectional imaging and an aging population, making adrenalectomy a progressively more common surgical procedure. Over the past three decades, adrenal surgery has undergone a paradigm shift from open adrenalectomy to minimally invasive (MI) techniques, with laparoscopic adrenalectomy becoming the standard approach for most benign and selected malignant adrenal tumors. More recently, retroperitoneoscopic and robotic approaches have expanded the armamentarium available to adrenal surgeons, allowing for tailored, patient-specific surgical strategies. This review summarizes current evidence on MI adrenalectomy techniques, including transperitoneal and retroperitoneal laparoscopic approaches, hand-assisted adrenalectomy, and robotic adrenalectomy, with particular emphasis on their role in pheochromocytoma and adrenocortical carcinoma. In addition, evolving ancillary technologies such as laparoscopic ultrasound, indocyanine green fluorescence imaging, artificial intelligence, and virtual and augmented reality are reviewed, highlighting their potential to enhance intraoperative decision-making, safety, and surgical precision. Current controversies, including the role of preoperative alpha-blockade, partial versus total adrenalectomy in hereditary pheochromocytoma, the oncologic adequacy of MI surgery for adrenocortical carcinoma, and the selective use of lymph node dissection, are discussed. Available evidence supports MI adrenalectomy as a safe and effective approach in carefully selected patients when performed by experienced surgeons in high-volume centers. Technological innovations continue to refine surgical planning, execution, and training, suggesting that the future of adrenal surgery will increasingly rely on precision-guided, personalized, and data-driven strategies. This review offers a timely and comprehensive synthesis of the evolving landscape of MI adrenalectomy, uniquely integrating current evidence across the full spectrum of surgical techniques with a critical appraisal of emerging ancillary technologies while addressing unresolved clinical controversies relevant to contemporary surgical practice. Full article

This systematic review examines how Extended Reality (XR) technologies, i.e., Virtual (VR), Augmented (AR), Mixed (MR), and Spatial Augmented Reality (SAR) are designed, implemented, and evaluated in cultural heritage (CH) applications, addressing five research questions: (RQ1) How were XR technologies applied in CH between 2021 and 2025? (RQ2) What interaction paradigms are used, and how do they shape engagement and meaning making? (RQ3) What user experience outcomes are reported in XR CH applications? (RQ4) What evaluation methods are employed and what methodological gaps remain? (RQ5) What challenges persist across XR heritage implementations? Peer-reviewed, English-language studies reporting on implemented XR systems in CH contexts with empirical or evaluative data were included; conceptual articles without a described implementation, non-English publications, and studies published before January 2020 were excluded. Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library were searched for publications dated January 2020 through March 2025, complemented by manual proceedings screening (SIGGRAPH, CHI, IMX, VRCAI) and backward/forward citation tracking. All databases were last searched in March 2025. Two independent researchers screened all records and extracted data; disagreements were resolved through structured discussion. Bias toward positive novelty outcomes was mitigated by including conference proceedings alongside journal articles to broaden the evidence base. A qualitative thematic synthesis was employed, as methodological heterogeneity across studies precluded statistical meta-analysis. Findings were organized inductively into four thematic domains through iterative coding and inter-author consensus. From an initial corpus of 359 records, 287 unique records were retained after deduplication; following title/abstract screening and full-text eligibility assessment, 64 studies were included in the final synthesis. The majority (60/64) were published between 2021 and 2025, with study sample sizes ranging from small expert cohorts (n ≈ 6) to large public deployments (n > 125). The thematic analysis across technology, interaction design, user experience, and evaluation reveals trends toward participatory, multiuser, and multimodal XR designs, reporting benefits including immersion, engagement, learning, and accessibility, alongside recurring challenges such as cost, usability, cybersickness, content authenticity, and lack of longitudinal evaluation. Beyond thematic description, using a cross-domain analytical synthesis, we identify the Design Coherence Framework for XR Heritage (DCF-XR); this is a four-dimensional interpretive model spanning technology, interaction design, user experience, and evaluation, which provides an original diagnostic lens for understanding the conditions under which XR effectively serves cultural heritage goals. A typology of four recurring design failure modes, derived inductively from the corpus, demonstrates that the most persistent shortcomings in the field arise not from the weakness of individual dimensions but from their misalignment with one another. Evidence is limited by the predominance of small convenience samples, single-session laboratory evaluations, and the absence of domain-specific standardized assessment instruments for XR in CH, which constrains the generalizability of reported outcomes. Targeted recommendations for rigorous, ethical, and inclusive XR practice in CH are presented, highlighting the need for longitudinal studies, open datasets, and standardized evaluation frameworks. This review received no external funding. This review was not pre-registered in a prospective register. Full article