Search Results (305)

In the study of sensory processes, the visual system has received the most research compared to other sensory systems. The primary difference between visual and auditory perception lies in the nature of the stimuli and the reception processes: vision perceives electromagnetic radiation, while auditory perception perceives acoustic signals of mechanical origin. This review aims to analyze modern approaches and controversies to the mechanisms of auditory perception related to psychophysics, psychophysiology, psychopathology, modern research on hearing in human–computer interaction (HCI) systems, and machine learning methods. Modern studies of acoustic patterns include a comprehensive assessment of the physical characteristics of perception, complex nonverbal auditory cues, verbalization, perception and memory, as well as individual differences in auditory perception. An analysis of the scientific literature allowed us to conclude that acoustic signals transformed in the brain into auditory images retain (encode) a number of amplitude-temporal parameters of acoustic signals that facilitate auditory discrimination (filtering), but interfere with auditory detection (recognition). Signal processing often, but not necessarily, involves brain regions involved in other forms of perception. It depends on subvocalization, includes semantically interpreted information and expectations, pictorial (visual) and descriptive components, functions as a mnemonic, and is linked to individual musical ability and experience (although the mechanisms of this connection are unclear). Full article

In ethics-sensitive product development, Generative AI can improve the efficiency of concept generation, but it also raises challenges related to accountability, value alignment, and decision transparency. To address limitations in current human-AI co-design processes, including unclear allocation of decision-making authority, insufficiently structured translation from design requirements to design constraints, and limited explainability in scheme evaluation, this study proposes an explainable Human–Computer Interaction (HCI)-based decision support framework for human-AI co-design, termed GAGT. The framework integrates Generative AI with multi-criteria decision-making methods. Specifically, the Analytic Hierarchy Process (AHP) is used to structure design requirements and determine their priorities, Grey Relational Analysis (GRA) is used to compare candidate schemes, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to support transparent final ranking. Within the framework, human designers are mainly responsible for requirement confirmation, priority judgment, review at key checkpoints, and final scheme selection, while AI mainly supports information organization, candidate scheme generation, and quantitative comparison. The framework was applied to the design of a community medical vehicle through a small-sample, case-based, quasi-experimental study. Compared with the human-only condition, the GAGT-supported condition reduced design time by 56.1%. Compared with the AI-autonomous condition, it showed no observed HIPAA violations and a Value Drift Index of 16.1%, indicating better consistency with human-defined priorities. The results suggest that the proposed framework may improve design efficiency while supporting clearer human oversight and decision explainability in Generative AI-assisted design, and may provide a structured approach to organizing human and AI roles in ethics-sensitive design tasks. Full article

Social Virtual Reality (VR) is emerging as a powerful medium for remote social interaction and collaboration, enabling multiple users to share experiences together while apart. Likewise, recent advances in multimedia technologies have proposed strategically combining diverse content formats and introducing interaction techniques for recreating virtual environments and engaging with them, respectively. This study pioneers the joint exploration of Social VR enhanced with holographic communication, multimodal content integration, and advanced interaction methods to deliver realistic and interactive group visits to reconstructed cultural heritage sites, specifically an existing restaurant–museum. The reconstructed space is further augmented with Points of Interest (PoIs), which can be freely visited and dynamically activated to provide rich contextual and historical information about the venue. The proposed technology and scenario have been evaluated objectively and subjectively. Results from objective tests offer relevant insights into the technical requirements, performance metrics (including bandwidth usage and latency), and overall system stability. Results from subjective tests with 22 participant pairs reveal high levels of user satisfaction, particularly in terms of immersion, presence, togetherness, and interaction quality regardless of whether participants acted as Guides (interacting with the VR environment) or Followers (observing and following the Guide’s actions). Beyond demonstrating feasibility, the findings from this study prove, for the first time, how strategically combining multi-user holoportation with multimodal content and role-based interactions can enable guided, collaborative cultural or touristic visits that preserve social presence while supporting rich exploration and contextual learning. Full article

City-scale augmented reality (AR) learning paths are outdoor, multi-stop educational routes delivered through mobile devices in public space. This paper examines the Art Nouveau Path, a mobile AR game (MARG) route in Aveiro, Portugal, as a deployable learning service. The focus is on implementation requirements and traceability rather than learning outcomes. The analysis combined profiling of eight points of interest (POIs) and 36 tasks, group-session logs from 118 sessions, and teacher-facing evidence from a validation workshop (T1-VAL, N = 30) and on-site observation (T2-OBS, N = 24). Open-text responses were segmented into meaning units and coded with an eight-determinant taxonomy, with good intercoder reliability (Krippendorff’s alpha = 0.83). Logs and the post-path questionnaire (S2-POST, N = 439) were used only to describe enactment feasibility and data integrity. The strongest determinants concerned onboarding and legibility, marker robustness and recovery, and curriculum alignment, together with safety and fallback needs. These signals were translated into 18 testable requirements linked to six transfer artefacts for enactment, maintenance, incident handling, and fallback. Overall, the study provides an implementation-oriented specification to support auditability, replication, and transfer in city-scale AR learning services. Full article

Competence-oriented Education for Sustainable Development requires evidence that immersive and gamified learning experiences elicit sustainability-relevant change beyond short pre–post windows. This study examines the Art Nouveau Path, a location-based mobile augmented reality heritage game implemented in Aveiro, Portugal, using a four-wave repeated cross-sectional design with anonymous student samples: baseline (S1-PRE, N = 221), immediate post-activity (S2-POST, N = 439, validated n = 438), follow-up (S3-FU, N = 434), and distant follow-up (S4-DFU, N = 69, validated n = 67). Analyses were anchored in a shared 25-item GreenComp-based questionnaire (GCQuest) block targeting Embodying Sustainability Values (ESVs; scale of 1 to 6) and combined distribution-aware descriptives, nonparametric omnibus, and pairwise tests with Holm correction, and planned robustness checks including equal-n downsampling and alternative scoring. Results displayed a pronounced post-activity peak (S2-POST), partial attenuation at follow-up (S3-FU), and convergence toward baseline at distant follow-up (S4-DFU), accompanied by loss of the high-agreement tail. Item-level contrasts suggested that later-wave declines concentrated in effortful self-regulation and critical appraisal items, whereas value endorsement items were more stable. These findings indicate that field-deployable mobile AR heritage paths may generate strong proximal competence-aligned signals; nevertheless, durable enactment-oriented change is likely to require structured reinforcement and integration into broader curricular sequences. Full article

The background to this research paper examines why people with disabilities often have additional problems with authentication (i.e., logging in to online services). While the primary focus is on accessible authentication, we also explore its relevance to electronic identification and consider the post-authentication stage of authorization (allowing continued use of a particular service once logged in). While people without disabilities regularly log into websites and applications without too much thought for the process, with an end-goal or task in mind to be achieved with the service that they are accessing, extra barriers exist for people with disabilities. We discover how there is a societal gap in terms of ease-of-use, as previous studies show that people with disabilities can find this step difficult, frustrating, or virtually impossible. For people who have a disability, complications will arise in this process, and we examine the nature of these problems identified by this group. A series of interviews (n = 15) is analyzed using Constructivist Grounded Theory methods to identify patterns in participants’ responses and develop a theory explaining why Accessible Authentication is a problem. While aiming to follow a constructivist methodology, this paper categorizes common traits revealed by participants in interviews. The key findings reveal that most users with disabilities say that the ability to authenticate effectively is reduced by accessibility barriers; in other words, participants felt hindered when logging in because of their disability. This leads us to conclude, with some degree of confidence, that the data implies a lack of accessibility for users of traditional authentication systems. A further area of concern for the participants is that maintaining security alongside ease-of-use was important to them (albeit with no clear winner between usability and security preferences), so future work on improving accessibility should ensure that users with disabilities’ information is not left vulnerable, while maintaining a sufficient level of accessibility for people with disabilities. Further to this, suggestions for achieving an accessible solution are presented in a preliminary Theoretical Framework. Full article

Real-time strategy (RTS) games provide a cognitively demanding and ecologically valid context for investigating workload dynamics in human–computer interaction (HCI). This multimodal study (HRV, NASA-TLX, behavior, interviews) examined multitasking, visual complexity, and decision pressure in 36 novice RTS players. High multitasking significantly increased subjective workload (total raw-TLX: from 22.50 ± 14.65 to 36.47 ± 20.19, p < 0.001) and prolonged completion time (from 317.17 ± 37.26 s to 354.92 ± 50.70 s, p < 0.001). Decision pressure elevated subjective workload (total raw-TLX: from 20 to 28, p = 0.008) without affecting performance. Although HRV did not consistently differentiate experimental conditions at the group level, it showed stable individual-level associations with perceived workload—both in expected directions (e.g., LF power positively correlated with total raw-TLX across four experiments, r = 0.28–0.53, all p < 0.05) and in inverse relationships that deviate from conventional stress models (e.g., stress index negatively correlated with total raw-TLX, r = −0.34 to −0.40, all p < 0.01). These findings suggest that autonomic responses in complex interactive environments may reflect dynamic engagement processes rather than uniform stress activation, supporting multimodal cognitive load assessment and offering transferable insights for interface design and workload evaluation in demanding HCI contexts. Full article

Sports have progressively incorporated technological advances, yet while the impact on performance and broadcasting is remarkable, the application of Artificial Intelligence (AI) in sports refereeing appears residual. A closer examination of prior research suggests that this limited development reflects deeper conceptual patterns within the field. While existing research on AI in sports officiating has predominantly conceptualized the field under an accuracy-optimization paradigm (focusing on decision precision, visual attention patterns, referee fatigue, and performance enhancement), there is a systematic lack of theoretical and empirical work that frames officiating as a broader socio-technical ecosystem. In particular, the literature does not provide conceptual models addressing (i) AI-assisted risk prevention and athlete safety as a core officiating function, (ii) human–AI task redistribution in cognitively overloaded and hybrid evaluative environments (e.g., disciplines such as artistic gymnastics or bodybuilding, where technical execution and aesthetic judgment are simultaneously assessed), and (iii) the redefinition of the referee’s role when AI operates as an anticipatory or real-time alert system rather than merely as a post hoc verification tool. Thus, the gap is not only one of application but of knowledge production: the dominant paradigm optimizes decision accuracy, yet it leaves the question of how AI can transform refereeing responsibilities, cognitive load distribution, and safety governance within competitive ecosystems under-theorized. This exploratory study adopts a Human–Computer Interaction (HCI) perspective to contrast existing initiatives with the practical expectations of professional referees. The methodology comprises two pillars: a systematic literature review following PRISMA guidelines and qualitative experimentation involving professional referees using focus groups and affinity diagrams techniques. From an initial total of 1251 records retrieved across five academic databases (2019–2025), 1122 articles were analyzed after applying strict inclusion/exclusion criteria. The findings provide preliminary support for our hypothesis of a significant underutilization gap, showing that research is concentrated on accuracy systems, while high-potential areas identified as critical by experts, such as athlete safety, represent only 0.6% of the analyzed literature. The study contributes a conceptual framework based on five categories established by experts, according to the identified use cases, providing guidance for future AI integration and interdisciplinary research in the sports officiating ecosystem. Based on the results, we point to future applications and lines of research aimed at integrating AI as a tool for sports refereeing. Full article

Digital solutions and social media platforms for older adults are widely associated with positive social and health-related outcomes. However, empirical evidence on their sustained use remains limited, particularly from a usability and accessibility perspective. Previous reviews have primarily examined social media use in later life from social or psychological perspectives, whereas this review focuses on digital inclusion and psychosocial well-being from a technological and ICT-oriented perspective. The aim is to examine how technological and design-related factors may help explain gaps between reported benefits and actual usage among older adults. It also seeks to support researchers and designers in understanding why many digital platforms fail to sustain long-term engagement despite reported initial benefits. The review further identifies areas where emerging approaches, such as AI- or VR/AR-supported (XR) systems, could be explored in future research. Following PRISMA 2020 guidelines, studies published between 2005 and 2025 were reviewed to identify key technological and user-related factors that influence online participation among older adults. Findings indicate that barriers related to digital literacy, accessibility, and usability are frequently associated with reduced engagement. At the same time, the potential role of user-friendly and well-designed platforms is often implied rather than empirically examined. Although inclusive and adaptable systems are widely discussed, explicit HCI- and interface-level usability evaluations remain surprisingly rare. None of the included studies examined AI- or XR-related features of social platforms. This indicates that ageing and social media research have yet to empirically address emerging technologies that increasingly shape online interaction. The review underscores the need for accessible and adaptable technological solutions that promote digital engagement and emotional well-being among older users. Full article

Predefined, user-independent gesture sets do not account for individual differences in movement patterns and physical limitations. This study presents a personalized wrist–forearm static gesture recognition system for human–computer interaction (HCI) using the Vicara Kai^TM wearable controller and a convolutional neural network (CNN). Unlike the system based on fixed, predefined gestures, the proposed approach enables users to define and train their own gesture sets. During gesture recording, users may either select a gesture pattern from a predefined prompt set or create their own natural, unprompted gestures. A dedicated software framework was developed for data acquisition, preprocessing, model training, and real-time recognition. The developed system was evaluated by optimizing the parameters of a lightweight CNN and examining the influence of sequentially applied changes to the input and network pipelines, including resizing the input layer, applying data augmentation, experimenting with different dropout ratios, and varying the number of learning samples. The performance of the resulting network setup was assessed using confusion matrices, accuracy, and precision metrics for both original gestures and gestures smoothed using the cubic Bézier function. The resulting validation accuracy ranged from 0.88 to 0.94, with an average test-set accuracy of 0.92 and macro precision of 0.92. The system’s resilience to rapid or casual gestures was also evaluated using the receiver operating characteristic (ROC) method, achieving an Area Under the Curve (AUC) of 0.97. The results demonstrate that the proposed approach achieves high recognition accuracy, indicating its potential for a range of practical applications. Full article

Background/Objectives: Adoption of user-centred design methods is essential in healthcare applications because it ensures that complex workflows are shaped around real users’ needs and behaviours, improving usability, accessibility, and sustainability. The use of user-centred design in healthcare applications still presents open challenges for identifying user requirements, including diverse stakeholder needs, limited user availability, complex interaction workflows, and organizational constraints. To address these challenges, this paper proposes a user-centred interaction design framework that systematically supports the identification and translation of user needs into actionable design requirements. Methods: The framework integrates user-centred design principles with generative tools, employing the Persona-and-Scenario method to transform user insights into actionable design requirements. By actively involving healthcare stakeholders, the framework ensures that both explicit and latent needs are captured. Results: The framework was implemented through two co-design events, which provided valuable feedback on data collection, visualization, interaction modalities, and privacy considerations. These insights were translated into functional, usability, and interface requirements for the Change Management Platform (CMP) for the KEEPCARING project. Conclusions: This framework introduces a structured, scenario-driven process that actively engages stakeholders in envisioning future states rather than merely refining existing systems. Its application demonstrates promising indications that it enhances requirement elicitation, promotes cross-stakeholder alignment, and yields higher-quality, contextually relevant design requirements. Full article

Most pre-trained language representation models are designed to encode contextualized semantic information for general language processing tasks. However, they are insufficient for math word problem (MWP) solving, which requires not only linguistic syntax and semantic understanding but also numerical reasoning. In this work, we introduce SSN4Solver, a deep neural solver that improves MWP-solving performance by symmetrically fusing syntax, semantics, and numeracy representations within its contextual encoder. Our approach jointly captures syntactic structures from dependency trees, semantic features from part-of-speech tags, and the attributes and relations of numerical entities. By treating these heterogeneous information sources in a balanced and aligned manner, SSN4Solver constructs a rich, multi-faceted representation for MWP solving without introducing substantial computational overhead, empowering human–computer interaction (HCI) applications such as adaptive educational interfaces and intelligent tutoring systems. Extensive experiments demonstrate that SSN4Solver outperforms existing baseline models. In addition, a visualization scheme is designed to elucidate how the three types of representations contribute to the solving process. SSN4Solver thus offers a scalable solution, contributing to the development of HCI systems that are both intelligent and mathematically effective. Full article

The integration of Internet of Things (IoT) and mobile computing in education offers new avenues to address complex health behaviors that affect cognitive performance. While traditional health education relies on passive information delivery, emerging research suggests that interactive systems can bridge the gap between intent and action. This study addresses the “double burden of malnutrition” in Ecuadorian schoolchildren (N = 120) as a Human-Computer Interaction (HCI) challenge. By utilizing a quantitative profiling approach rooted in the Social Dimensions of Health framework, we modeled the user requirements for a proposed intervention system. The findings identified a critical “Action Gap”: while 78.3% of users possess the motivation to improve habits for academic gain, 53.3% remain entrenched in high-sugar consumption patterns due to environmental latency. Statistical profiling reveals a significant dissonance ($p<0.05$) between cognitive intent and behavioral execution. Consequently, this paper presents the “Digital Bridge Architecture,” a computational framework that leverages these motivation metrics to design an Alternate Reality Game (ARG) logic. We conclude that conventional static applications may be limited in their capacity to support sustained behavioral change in this context. The proposed framework suggests that context-aware, gamified feedback mechanisms can offer a promising direction for aligning academic motivation with healthier behavioral outcomes. Full article

Eye tracking has become a central method in human–computer interaction (HCI), supported by advances in sensing technologies and AI-based gaze analysis. Despite this rapid growth, a comprehensive and up-to-date overview of eye-tracking research across the broader HCI landscape remains lacking. This study combines records from Web of Science (WoS) and Scopus to analyse 1033 publications on eye tracking in HCI published between 2020 and 2025. After merging and deduplicating the datasets, we conducted bibliometric network analyses (keyword co-occurrence, co-citation, co-authorship, and source mapping) using VOSviewer and performed a qualitative content analysis of the 50 most-cited papers. The literature is dominated by journal articles and conference papers produced by small- to medium-sized research teams (mean: 3.9 authors per paper; h-index: 29). Keyword and overlay visualisations reveal four principal research axes: deep-learning-based gaze estimation; XR-related interaction paradigms within HCI; cognitive load and human factors; and usability- and accessibility-oriented interface design. The most-cited studies focus on gaze interaction in immersive environments, deep learning for gaze estimation, multimodal interaction, and physiological approaches to assessing cognitive load. Overall, the findings indicate that eye tracking in HCI is evolving from a measurement-oriented technique into a core enabling technology that supports interaction design, cognitive assessment, accessibility, and ethical considerations such as privacy. This review identifies research gaps and outlines future directions for benchmarking practices, real-world deployments, and privacy-preserving gaze analytics in HCI. Full article

Authentic student projects in higher education reflect plausible real-world scenarios, connecting the curriculum to students’ future careers. In Human–Computer Interaction, this is realised through focus on real-world design and evaluation problems, which offer similar challenges to those found in industry settings. A limitation, however, is seen in the teaching of participant recruitment, with students typically choosing convenience samples of friends and family as their research participants rather than developing a balanced and budget-conscious recruitment strategy. This paper presents Recruitment Rush, a boardgame designed specifically to meet this challenge, presenting a meaningful real-world recruitment scenario that can be played in under an hour to stimulate understanding of how to recruit participants for a study. Evaluation of the game with 95 students and academics shows that the game is engaging and invites conversation and reflection on the nature of participant recruitment even beyond the direct HCI context. Full article