Search Results (892)

Urban mobility plays a vital role in shaping sustainable cities, yet the effectiveness of public transportation is often undermined by poor last-mile connectivity (LMC). In the National Capital Region (NCR) of Delhi, despite the Delhi Metro Rail serving as a key transit system, limited integration with surrounding areas hinders accessibility, which particularly affects women, elderly adults, and socioeconomically disadvantaged groups. This study evaluates LMC performance at two key metro stations, Nehru Place and Botanical Garden, using a mixed-methods approach that includes user surveys, spatial survey, thematic analysis, and infrastructure scoring across five critical pillars: accessibility, safety and comfort, intermodality, service availability, and inclusivity. The findings communicate notable contrasts. Botanical Garden exhibits strong intermodal linkages, pedestrian-friendly design, and supportive signage, while Nehru Place indicates a need for infrastructural improvements, safety advancement and upgrades, and strengthened universal design features. These disparities limit effective metro usage and discourage a shift from private to public transport. The study highlights the importance of user-centered, multimodal solutions and the need for cohesive urban governance to address LMC gaps. By identifying barriers and opportunities for improvement, this research paper contributes to the formulation of more inclusive and sustainable urban transport strategies in Indian metropolitan regions. Full article

Background/Objectives: Mpox, a viral disease marked by distinctive skin lesions, has emerged as a global health concern, underscoring the need for scalable, accessible, and accurate diagnostic tools to strengthen public health responses. This study introduces ITMA’INN, an AI-driven healthcare system designed to detect Mpox from skin lesion images using advanced deep learning. Methods: The system integrates three key components: an AI model pipeline, a cross-platform mobile application, and a real-time public health dashboard. We leveraged transfer learning on publicly available datasets to evaluate pretrained deep learning models. Results: For binary classification (Mpox vs. non-Mpox), Vision Transformer, MobileViT, Transformer-in-Transformer, and VGG16 achieved peak performance, each with 97.8% accuracy and F1-score. For multiclass classification (Mpox, chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy skin), ResNetViT and ViT Hybrid models attained 92% accuracy (F1-scores: 92.24% and 92.19%, respectively). The lightweight MobileViT was deployed in a mobile app that enables users to analyze skin lesions, track symptoms, and locate nearby healthcare centers via GPS. Complementing this, the dashboard equips health authorities with real-time case monitoring, symptom trend analysis, and intervention guidance. Conclusions: By bridging AI diagnostics with mobile technology and real-time analytics, ITMA’INN advances responsive healthcare infrastructure in smart cities, contributing to the future of proactive public health management. Full article

Human–machine trust has shifted over the past three decades from trust in automation to trust in AI, while research paradigms, disciplines, and problem spaces have expanded. Centered on AI in healthcare, this narrative review offers a longitudinal synthesis that traces and compares phase-specific changes in theory and method, providing design guidance for human-AI systems at different stages of maturity. From a cross-disciplinary view, we introduce an Interdisciplinary Human-AI Trust Research (I-HATR) framework that aligns explainable AI (XAI) with human–computer interaction/human factors engineering (HCI/HFE). We distill three core categories of determinants of human-AI trust in healthcare, user characteristics, AI system attributes, and contextual factors, and summarize the main measurement families and their evolution from self-report to behavioral and psychophysiological approaches, with growing use of multimodal and dynamic evaluation. Finally, we outline key trends, opportunities, and practical challenges to support the development of human-centered, trustworthy AI in healthcare, emphasizing the need to bridge actual trustworthiness and perceived trust through shared metrics, uncertainty communication, and trust calibration. Full article

As self-service technologies (SSTs) such as kiosks become embedded in service infrastructure, understanding the socio-cognitive drivers of adoption has grown in importance. This study extends the Technology Acceptance Model (TAM) by integrating Digital Readiness (DR), Trust in Technology (TT), Perceived Usefulness (PU), and Perceived Barriers (PB) into a single framework, and tests it using structural equation modeling (SEM) with survey data from 750 kiosk users in China. TT emerges as the strongest direct predictor of intention to use (IU) and also increases PU while reducing PB. The deterrent effect of PB exceeds the positive effect of PU. DR promotes adoption indirectly by raising TT and PU and lowering PB, whereas its direct path to IU is negative, suggesting a tension between readiness and heightened expectations. Multi-group analyses show that non-digital natives and low-frequency users are more sensitive to trust-related factors, whereas digital natives and high-frequency users respond more to barrier reduction. These findings integrate trust and barrier perspectives into TAM and reconceptualize DR as an ambivalent antecedent. Practically, a segment- and journey-oriented design frame centered on trust and friction provides a common reference for aligning kiosk design, KPIs, and investment decisions across industries. Full article

Natural and unconstrained locomotion remains a fundamental challenge in creating truly immersive virtual reality (VR) experiences. This paper presents the design and control of a novel robotic omnidirectional treadmill (ODT) based on the bilateral symmetry of two cooperative five-bar planar mechanisms designed to replicate realistic walking mechanics. The central contribution is a human in the loop control strategy designed to achieve stable walking in place. This framework employs a specific control strategy that actively repositions the footplates along a dynamically defined ‘Line of Movement’ (LoM), compensating for the user’s motion to ensure the midpoint between the feet remains stabilized and symmetrical at the platform’s geometric center. A comprehensive dynamic model of both the ODT and a coupled humanoid robot was developed to validate the system. Numerical simulations demonstrate robust performance across various gaits, including turning and catwalks, maintaining the user’s locomotion center with a maximum resultant drift error of 11.65 cm, a peak value that occurred momentarily during a turning motion and remained well within the ODT’s safe operational boundaries, with peak errors along any single axis remaining below 9 cm. The system operated with notable efficiency, requiring RMS torques below 22 Nm for the primary actuators. This work establishes a viable dynamic and control architecture for foot-tracking ODTs, paving the way for future enhancements such as haptic terrain feedback and elevation simulation. Full article

The building sector plays a key role in the transition toward climate neutrality, with national regulations across the EU requiring the construction of nearly zero-energy buildings (nZEBs). However, while energy performance has been extensively studied, less attention has been given to the problem of ensuring user comfort—both indoors and in the surrounding outdoor areas—under nZEB design constraints. This gap raises two key research objectives: (1) to evaluate whether a well-designed nZEB with extensive glazing maintains acceptable indoor thermal comfort and (2) to assess whether residents experience greater outdoor thermal comfort and satisfaction in small, sun-exposed private gardens or in larger, shaded communal green spaces. To address these objectives, a newly built residential estate near Kraków (Poland) was analyzed. The investigation included simulation-based assessments during the design phase and in situ measurements during building operation, complemented by a user survey on spatial preferences. Indoor comfort was evaluated for rooms with large glazed façades, as well as rooms with standard-sized windows, while outdoor comfort was assessed in both private gardens and a shared green courtyard. Results show that shading the southwest-oriented glazed façade with an overhanging terrace provided slightly lower temperatures in ground-floor rooms compared to rooms with standard unshaded windows. Outdoors, users experienced lower thermal comfort in small, unshaded gardens than in the larger, vegetated communal area (pocket park), which demonstrated greater capacity for temperature moderation and thermal stress reduction. Survey responses further indicate that potential future residents prefer the inclusion of a shared green–blue infrastructure area, even at the expense of building some housing units in semi-detached form, instead of maximizing the number of detached units with unshaded individual gardens. These findings emphasize the importance of addressing both indoor and outdoor comfort in residential nZEB design, showing that technological efficiency must be complemented by user-centered design strategies. This integrated approach can improve the well-being of residents while supporting climate change adaptation in the built environment. Full article

The future of anxiety management lies in bridging traditional evidence-based treatments with intelligent and adaptive digital platforms. Embedding multi-agent systems capable of real-time mood detection and self-management support represents a transformative step towards intelligent care, enabling users to independently regulate acute episodes, prevent relapse, and promote sustained personal well-being. These digital solutions illustrate how technology can improve accessibility, personalization, and adherence, while establishing the foundation for integrating multi-agent architectures into mental health systems. Such architectures can continuously detect and interpret users’ emotional states through multimodal data, coordinating specialized agents for monitoring, personalization, and intervention. Crucially, they extend beyond passive data collection to provide active, autonomous support during moments of heightened anxiety, guiding individuals through non-pharmacological strategies such as breathing retraining, grounding techniques, or mindfulness practices without requiring immediate professional involvement. By operating in real time, multi-agent systems function as intelligent digital companions capable of anticipating needs, adapting to context, and ensuring that effective coping mechanisms are accessible at critical moments. This paper presents a multi-agent architecture for the digital management of anxiety episodes, designed not only to enhance everyday well-being but also to deliver immediate, personalized assistance during unexpected crises, offering a scalable pathway towards intelligent, patient-centered mental health care. Full article

Antimicrobial resistance (AMR) poses a significant global threat to human and animal health, necessitating robust surveillance and stewardship tools. While existing systems address aspects of veterinary AMR, a comprehensive, user-centric dashboard for U.S. veterinarians remains a critical unmet need. This study aimed to identify U.S. veterinarians’ preferences and perceived needs for such a dashboard, to help guide its design and development. A cross-sectional survey was conducted between January and March 2024, targeting U.S. veterinarians through professional channels. The survey instrument captured demographics, experiences with existing tools, preferences for data types and visualizations, desired technical specifications, and open-ended feedback. Of the 677 respondents, a near-unanimous consensus (over 75%) emerged on the importance of functionalities like antimicrobial stewardship education, off-label use guidance, surveillance data, and empirical treatment support. Over 70% expressed comfort sharing aggregated geographic and de-identified animal data. A strong preference was observed for making the dashboard accessible by veterinary colleges (78.87%), diagnostic laboratories (72.61%), and federal agencies (USDA: 71.47%, CDC: 66.67%, FDA: 62.11%), indicating a desire for a collaborative, authoritative system. The findings provide a robust foundation for developing a U.S. veterinary AMR dashboard. Future phases should adopt an iterative, user-centered design, incorporating qualitative research with diverse stakeholders and piloting a prototype with preferred institutional partners. This approach will ensure a trusted, sustainable tool that effectively translates surveillance data into actionable insights for improved animal and public health. Full article

This Scoping Review (ScR) synthesizes advances in architectural eye-tracking (ET) research published between 2010 and 2024. Drawing on 75 peer-reviewed studies that met clear inclusion criteria, it monitors the field’s rapid expansion, from only 20 experiments before 2018, to more than 45 new investigations in the three years thereafter, situating these developments within the longer historical evolution of ET hardware and analytical paradigms. The review maps 13 recurrent areas of application, focusing on design evaluation, wayfinding and spatial navigation, end-user experience, and architectural education. Across these domains, ET reliably reveals where occupants focus, for how long, and in what sequence, providing objective evidence that complements designer intuition and conventional post-occupancy surveys. Experts and novices might display distinct gaze signatures; for example, architects spend longer fixating on contextual and structural cues, whereas lay users dwell on decorative details, highlighting possible pedagogical opportunities. Despite these benefits, persistent challenges include data loss in dynamic or outdoor settings, calibration drift, single-user hardware constraints, and the need to triangulate gaze metrics with cognitive or affective measures. Future research directions emphasize integrating ET with virtual or augmented reality (VR) (AR) to validate design interactively, improving mobile tracking accuracy, and establishing shared datasets to enable replication and meta-analysis. Overall, the study demonstrates that ET is maturing into an indispensable, evidence-based lens for creating more intuitive, legible, and human-centered architecture. Full article

With the rapid advancement of artificial intelligence, the deployment of social robots has significantly broadened, extending into diverse fields such as education, medical services, and business. Despite this expansive growth, there remains a notable scarcity of empirical research addressing the underlying psychological mechanisms that influence human–robot interactions. To address this critical research gap, the present study proposes and empirically tests a theoretical model designed to elucidate how users’ multi-dimensional perceived values of social robots influence their attitudinal responses and outcomes. Based on questionnaire data from 569 social robot users, the study reveals that users’ perceived utilitarian value, emotional value, and hedonic value all exert significant positive effects on active involvement, thereby fostering their identification and reinforcing attitudinal loyalty. Among these dimensions, emotional value emerged as the strongest predictor, underscoring the pivotal role of emotional orientation in cultivating lasting human–robot relationships. Furthermore, the findings highlight the critical mediating function of active involvement in linking perceived value to users’ psychological sense of belonging, thereby elucidating the mechanism through which perceived value enhances engagement and promotes sustained long-term interaction. These findings extend the conceptual boundaries of human–machine interaction, offer a theoretical foundation for future explorations of user psychological mechanisms, and inform strategic design approaches centered on emotional interaction and user-oriented experiences, providing practical guidance for optimizing social robot design in applications. Full article

This paper presents the design, development, and experimental evaluation of a hybrid wheel–leg guide robot intended to assist blind and elderly people with mobility tasks indoors and outdoors. The design requirements are derived from an analysis of safety, usability, and affordability needs for assisting devices. The resulting design consists of a compact platform with two front leg–wheel assemblies and three additional wheels, two of which are motorized, arranged in a triangular configuration that provides stable support and reliable traction. The proposed locomotion system is innovative because existing guide robots typically rely exclusively on either wheels or legs. In contrast, this hybrid configuration combines the energy efficiency of wheeled locomotion with the capability of leg-assisted stepping, enabling improved terrain adaptability. Experiments with a prototype were carried out in indoor environments, including straight-line motion, turning, and obstacle-overcoming tests. The prototype, with a total weight of 1.9 kg and a material cost of 255 euros, maintained stable movement and achieved a 100% success rate for obstacles up to 30 mm, with partial success up to 40 mm. Additional test results indicate an average cruising speed of 0.1 m/s, and a practical endurance of 4.5–5 h. The proposed design aims to contribute to the development of more inclusive, efficient, and user-centered robotic solutions, promoting greater autonomy and quality of life for blind and elderly people. Full article

The growing adoption of modular robotic systems presents new challenges in ensuring ease of assembly, deployment, and reconfiguration, especially for end-users with varying technical expertise. This study proposes and validates an Assembly Complexity Index (ACI) framework, combining subjective workload (NASA Task Load Index) and task complexity (Task Complexity Index) into a unified metric to quantify assembly difficulty. Twelve participants performed modular manipulator assembly tasks under supervised and unsupervised conditions, enabling evaluation of learning effects and assembly complexity dynamics. Statistical analyses, including Cronbach’s alpha, correlation studies, and paired t-tests, demonstrated the framework’s internal consistency, sensitivity to user learning, and ability to capture workload-performance trade-offs. Additionally, we propose an augmented reality (AR) and virtual reality (VR) integration workflow to further mitigate assembly complexity, offering real-time guidance and adaptive assistance. The proposed framework not only supports design iteration and operator training but also provides a human-centered evaluation methodology applicable to modular robotics deployment in Industry 4.0 environments. The AR/VR-assisted workflow presented here is proposed as a conceptual extension and will be validated in future work. Full article

Open-plan offices have established themselves as economically efficient working environments and promote communication. Zoned lighting concepts have proven to be particularly energy-efficient and are determined by the respective occupancy profile. Due to their size, open-plan offices usually have very different levels of daylight availability depending on their position in the room, which affects the light doses per workstation. It is unclear what influence the distribution of users in the room has on the respective target values. This study therefore examines the effects of a variation in the spatial distribution of users in a real open-plan office regarding the three target values of communication distances, daily light doses, and artificial light energy requirements. Statistical methods are used to examine how a user distribution optimized for one target variable affects the other target variables. Since optimizing user distribution is an NP-hard combinatorial problem, heuristic methods are used. The results show that optimized user distribution improves only one target variable. There are no consistently strong correlations between the optimization of communication distances, energy savings, and achievable daily light doses. The work thus contributes to the holistic design of sustainable, user-centered working environments. This research is an example of a living lab case study with optimization-based modeling, emphasizing its exploratory nature rather than controlled experimental inference. Full article

Responsiveness—the speed at which a text-to-speech (TTS) system produces audible output—is critical for real-time voice assistants yet has received far less attention than perceptual quality metrics. Existing evaluations often touch on latency but do not establish reproducible, open-source standards that capture responsiveness as a first-class dimension. This work introduces a baseline benchmark designed to fill that gap. Our framework unifies latency distribution, tail latency, and intelligibility within a transparent and dataset-diverse pipeline, enabling a fair and replicable comparison across 13 widely used open-source TTS models. By grounding evaluation in structured input sets ranging from single words to sentence-length utterances and adopting a methodology inspired by standardized inference benchmarks, we capture both typical and worst-case user experiences. Unlike prior studies that emphasize closed or proprietary systems, our focus is on establishing open, reproducible baselines rather than ranking against commercial references. The results reveal substantial variability across architectures, with some models delivering near-instant responses while others fail to meet interactive thresholds. By centering evaluation on responsiveness and reproducibility, this study provides an infrastructural foundation for benchmarking TTS systems and lays the groundwork for more comprehensive assessments that integrate both fidelity and speed. Full article

With the increasing depth of research on built environments, theories of restorative environments and concepts of biophilic design have garnered widespread attention in the field of architecture. Based on Attention Restoration Theory (ART) and Flow Theory, this study systematically investigates how the architectural environment of aquatic centers influences users’ learning engagement in sports through psychological mechanisms. Analysis of cross-sectional data from 865 users revealed that all four core dimensions of restorative environments (being away, extent, fascination, and compatibility) significantly positively affect users’ learning engagement in aquatic centers. Psychological flow was found to mediate the relationship between these restorative dimensions and learning engagement. Building on previous research, this study constructs a theoretical framework of “restorative design–flow experience–learning behavior”, integrating the architectural features of aquatic centers with users’ psychological experiences. This approach addresses the gap in existing research where architectural elements and user psychological experiences have been studied in isolation, providing a theoretical basis for optimizing user experience through environmental interventions in sports architecture. The findings extend the application of environmental psychology in sports architecture and offer practical guidance for designing aquatic environments that promote learning engagement. Full article