Search Results (373)

Background: Cervicogenic headache (CEH) is a secondary headache disorder originating from dysfunction in the cervical spine. In addition to pain, individuals with CEH frequently experience disturbances in postural control and sensorimotor integration, which may compromise functional capacity and quality of life. Conventional clinical assessments typically focus on pain intensity and cervical range of motion; however, these measures often fail to capture the multifactorial mechanisms underlying balance impairments in this population. Machine learning (ML) methods offer the ability to integrate multidimensional clinical data and may provide a more comprehensive approach for identifying patterns of postural stability and the factors influencing balance regulation in CEH. Methods: A secondary analysis was conducted using baseline data pooled from three registered randomized controlled trials, comprising 68 independent participants diagnosed by a neurologist according to the International Classification of Headache Disorders, 3rd edition (ICHD-3). Postural Stability Class served as the primary outcome and was derived from quantitative stability scores categorized as High, Moderate, or Low. Predictor variables included demographic characteristics (age, gender), clinical measures (pain intensity, headache frequency, symptom duration, cervical range of motion), and sensorimotor parameters (center-of-pressure sway and gaze accuracy). Five machine learning algorithms—Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and Gradient Boosting—were trained and evaluated using 10-fold cross-validation with procedures implemented to reduce overfitting. Results: The Gradient Boosting classifier demonstrated the best performance, achieving an accuracy of 0.857 and an F1 score of 0.857, with a cross-validated accuracy of 0.802 ± 0.063. Random Forest and XGBoost achieved accuracies of 0.786. Feature importance analysis identified center-of-pressure sway and pain intensity as the most influential predictors of stability classification, followed by cervical flexion range of motion and gaze accuracy. Demographic variables showed minimal contribution to model performance. Conclusions: Machine learning models were able to distinguish different levels of postural stability in individuals with CEH. The findings highlight the central role of pain and sensorimotor control in balance regulation and suggest that predictive analytics may support precision physiotherapy by enabling rehabilitation strategies tailored to individual sensorimotor profiles. Full article

Ocular ductions deform the optic disc and peripapillary blood vessels, and deformations can be interpreted as mechanical strain. We used confocal scanning laser ophthalmoscopy (cSLO) to map strain in disc and peripapillary retinal vessels associated with the cardiac pulse and determine if such strain is influenced by gaze direction. Sets of 13 infrared cSLO images were obtained sequentially for each eye using a Heidelberg Spectralis scanner in cinematic mode over a 3 sec interval in adults. Imaging was repeated in central, and horizontally (30° adduction/abduction) and vertically eccentric gazes (10° supraduction/infraduction). Retinal vessels, optic disc, and fovea were segmented using custom-trained, deep learning-based models. Frame to frame vascular displacements were automatically determined using optical flow analysis, allowing computation of equivalent strain. A total of 25 eyes of 13 subjects of mean age 39 ± 18 (standard deviation, range: 25 to 81) years were included. Average equivalent strain over 3 sec ranging from 0.27% to 0.36% exceeded the 0.16% noise threshold across all gazes and regions, indicating measurable pulse-induced deformation. After adjustment for age and axial length, pulsatile maximum and minimum strain were influenced slightly by gaze direction, maximally for supraduction, whereas mean strain did not vary significantly with gaze. The cardiac pulse induces measurable deformation of retinal vessels that can be quantified as equivalent strain in the image plane using optical flow-derived displacement fields. However, the interaction of pulse strain with gaze direction is unlikely to be a significant confound for investigations of strains associated with eye movements. Full article

This article argues that lotus and dharma wheel motifs in the Dunhuang Mogao Caves function not merely as decorative symbols but as active visual apparatuses that generate embodied religious experience through a mechanism we term “totemic mediation.” Drawing on Lévi-Strauss’s structuralist reading of totemism, Descola’s ontological framework, Gell’s theory of art as agency, Meyer’s “sensational form,” and Varela’s neurophenomenology, we define totemic mediation as a triadic mechanism encompassing material–spatial arrangement, ontological transformation of experiential states, and value structure generation. We analyze motifs from Mogao Caves 285, 329, and 361 using a five-step analytic framework: formal–visual description, reconstructed embodied viewing, doctrinal identification, mediation mechanism analysis, and evaluative assessment. The analysis demonstrates that the lotus mediates ontologically along a spatial axis, building a vertical channel between the worldly and the divine through ceiling configurations and upward gazes, while the dharma wheel mediates teleologically across the temporal axis, neutralizing linear temporality through rotational dynamics. Together, these motifs constitute “visual prajñā”—a nonconceptual, embodied cognitive effect that bypasses discursive reasoning to enable direct apprehension of śūnyatā (emptiness). This article offers a replicable analytic framework for examining how religious images operate simultaneously as visual apparatuses and ontological mediators. Full article

Background/Objectives: Although early institutionalization has been linked to socioemotional difficulties, its relationship with interoception in early childhood remains unclear. This study examined differences in interoception, emotion regulation, and prosocial behavior between institutionalized preschool children (IPC) and noninstitutionalized preschool children (NIPC) and explored the associations among these domains. Methods: In total, 51 children aged 4–6 years (26 IPC, 25 NIPC) participated in this study. Interoceptive accuracy (IAc) was assessed using an adapted Jumping Jack Paradigm that combined subjective reports and objective heart rate measures. Interoceptive sensitivity was evaluated using the iBEAT task based on gaze duration toward synchronous and asynchronous stimuli. Cooperation was measured using a joint fishing task, and emotion regulation was assessed using a delayed gratification task and the Early Emotion Regulation Behavior Questionnaire. Group differences were analyzed using one-way analysis of variance. Regression analyses were performed to explore the associations among variables. Results: Both groups had IAc values close to zero, indicating overall correspondence between subjective and objective signals. However, IPC showed more negative values, indicating underestimation, whereas NIPC showed more positive values, indicating overestimation. No significant differences in interoceptive sensitivity were found, and no evidence of discrimination between synchronous and asynchronous stimuli emerged. Compared with the IPC, the NIPC exhibited greater cooperation. No group differences were found in inhibitory control, although differences were observed in specific emotion regulation strategies. Regression analyses indicated that institutionalization and interoceptive sensitivity predicted IAc, whereas emotion regulation strategies and synchronous preference predicted cooperation. Conclusions: The results suggest that early institutionalization may induce changes in interoception, emotion regulation, and cooperation. Full article

Mixed reality (MR) technologies enable users to experience computer-generated content within the physical environment through spatial computing and head-mounted displays. By supporting real-time interaction through speech, gesture, gaze, and movement, MR offers new opportunities for game design beyond productivity and educational applications. However, relatively few studies have examined interaction modalities in MR games. In this paper, we present the design and deployment of four MR games on the Microsoft HoloLens 2: three that use monomodal input (speech, gaze, or gesture) and one that uses multimodal input (speech, gaze, and gesture). We conducted a study with ten participants and evaluated player experience using subjective self-reports of task load, emotional engagement, and comfort alongside objective measures, namely brain activity data collected with a five-channel electroencephalogram (EEG) device. Our preliminary findings suggest two clusters of interaction modalities based on subjective measures, a pattern that is also reflected in the objective EEG measures. Our analysis combining subjective and EEG data indicates that interaction modality influences task load and emotional engagement. Additionally, our functional connectivity analysis showed links in activity across the prefrontal, temporal, and occipital brain regions for different input modalities in the MR games. Full article

Background: The conventional Hess–Lancaster test is widely used to assess ocular misalignment across diagnostic gaze positions, but it relies on subjective responses and manual recording. Virtual reality may provide a more standardized framework for ocular motility assessment. Objectives: To evaluate the agreement and within-method repeatability of point-by-point deviation measurements obtained with the conventional Hess–Lancaster test and a VR-based Hess–Lancaster assessment implemented in Dicopt Pro. Methods: This cross-sectional observational study included 52 adults with suspected or diagnosed ocular motility disorders. Participants underwent both assessments using the same predefined gaze positions. Agreement was assessed using Bland–Altman analysis, concordance correlation coefficients, mean absolute differences, and mixed-effects modeling. Repeatability was evaluated in a subset with repeated measurements using session-to-session differences and intraclass correlation coefficients. Results: The VR-based assessment showed moderate agreement with the conventional test, with a mean concordance correlation coefficient of 0.57 for both eyes. Mean bias was 1.22 prism diopters for the right eye and 0.10 prism diopters for the left eye. Repeatability estimates were moderate-to-good, with ICC values ranging from 0.62 to 0.83, although repeated measurements were available only in a subset of participants. Conclusions: The VR-based Hess–Lancaster assessment showed small mean differences and moderate agreement with the conventional test, although both methods should be interpreted within the context of the complete clinical examination. Full article

With the development of human–computer interaction technology, non-contact interaction based on gaze tracking and facial movements has become a research hotspot. Traditional mouse-and-keyboard methods pose challenges for people with disabilities or limited hand movements, while existing gaze-tracking systems often rely on expensive hardware or lack sufficient accuracy. This paper designs and implements a real-time system using ordinary cameras, achieving natural, efficient interaction via multimodal input combination. The system uses an improved MobileNetV2 backbone to construct GazeTrackNet for gaze estimation. It adopts MediaPipe Face Mesh to detect facial landmarks. Meanwhile, it applies geometric feature analysis, including eye aspect ratio and mouth aspect ratio, to identify actions such as blinking and mouth opening. It adopts a hybrid control strategy that combines gaze jumping and head fine-tuning, using mouth state as the main control switch. Key contributions include a lightweight gaze-tracking algorithm that enables stable and efficient gaze detection on consumer-grade hardware, a multimodal interaction strategy based on facial movement that improves system stability and ease of use, and a complete prototype system that achieves real-time performance on standard laptops. Experimental results show an average gaze average angle error of 3.0°, 97% eye state recognition accuracy, and end-to-end latency below 70 ms. The system can satisfy the requirements of daily desktop interaction under normal indoor lighting, and shows potential for future barrier-free interaction applications after further validation with target users. Existing gaze-tracking methods either suffer from low precision on lightweight devices or bring heavy computational overhead. Common facial recognition approaches also face frequent false trigger interference. Compared with them, our scheme achieves balanced accuracy and real-time performance via an attention-enhanced structure, and the designed dual anti-shake mechanism effectively suppresses misjudgment, delivering a more stable hands-free interaction experience. Full article

Smartphone-holder use during motorcycling is increasingly common, but its task-dependent ergonomic effects remain insufficiently understood. This study examined visual, postural, and muscular responses during smartphone-holder use under a simulated riding-posture condition. Forty healthy adults completed five smartphone-use tasks: dynamic viewing, static viewing, texting, seated use, and standing use. Each riding-related task condition lasted one minute, with the final 30 s designated as the stable data collection window. For postural variables, instantaneous values were recorded at four time points (0, 10, 20, and 30 s from the onset of the stable window) and averaged. For electromyography (EMG), integrated EMG (IEMG) was computed over the same 30 s window using ten consecutive non-overlapping 3 s epochs, and averaged for normalization. The neck flexion (NF), upper thoracic angle (UTA), gaze angle (GA), viewing distance (VD), and electromyographic activities of the cervical erector spinae (CES) and upper trapezius (UTZ) were measured using integrated motion-analysis and EMG approaches. Two-way mixed ANOVA and repeated-measures correlation analyses were performed. The task condition significantly affected all measured variables, with effect sizes ranging from moderate to large (all ηp² ≥ 0.155), with texting producing the greatest NF, shortest VD, and highest muscle activation. Strong within-subject associations were identified among visual, postural, and muscular variables across riding-related tasks (VD–NF: r = −0.815, p < 0.001). Females exhibited higher CES and UTZ activation than males. These findings reveal a task-dependent visual–postural–muscular co-variation pattern during scooter-mounted smartphone-holder use and support the application of a sensor-based ergonomic assessment for characterizing task-dependent visual–postural–muscular responses during scooter-mounted smartphone-holder use. Full article

Physicians routinely operate in environments that require the rapid processing of complex and dynamic visual information to diagnose patient conditions, communicate effectively, and make informed decisions. Despite the central role of visual attention in clinical practice, these processes are rarely conceptualized or systematically measured in medical education research. In other professional domains, such abilities are described as professional vision (PV)—the situated capacity to selectively attend to relevant cues and interpret them considering domain-specific knowledge. Although the term professional vision foregrounds visual attention, we use it here to cover the multimodal clinical perception in which visual cues are typically embedded—predominantly visual, but in many tasks also auditory and verbal—with visual attention as the analytic anchor. This paper introduces a cognitive process model of professional vision for medical education (PV-CP) that specifies the perceptual and cognitive subprocesses underlying how physicians perceive and interpret clinically relevant information. Building on this model, we propose a theory-driven framework for the measurement of professional vision using multimodal indicators. Central to our argument is the assumption that professional vision represents a latent, temporally unfolding construct that cannot be validly captured through single behavioral metrics or outcome measures. Instead, robust measurement requires the coordinated analysis of gaze-based indicators of visual attention and cognitive indicators of reasoning, each reflecting distinct subprocesses of professional vision. By systematically linking families of indicators to specific subprocesses and clarifying their respective inferential strengths and limitations, the PV-CP model advances a process-oriented approach to studying professional vision in medical education. The framework provides a conceptual basis for integrating multimodal data sources and supports more precise interpretations of gaze and reasoning data in expertise research. In doing so, the model contributes to the theoretical refinement of professional vision and offers a structured foundation for future empirical research and the design of learning environments aimed at fostering clinically relevant perceptual–cognitive skills. Full article

This study investigates how different rural landscape types shape visual attention and physiological responses, with the aim of informing more targeted rural landscape renewal. Four typical rural landscape types in the suburbs of Hefei, China, were examined: Flat Farmland (FF), Hilly Forest (HF), Developed Plain (DP), and Water-network Lowland (WNL). All four study villages are project villages in the suburban area of Hefei where rural revitalization is currently being advanced. This study therefore treats them as empirical cases within the context of rural revitalization in China, using them to examine perceptual differences among rural landscape types and their implications for rural landscape renewal. A two-stage research design was adopted to balance field realism and laboratory control. In the first stage, 40 representative scene images were selected by combining field video records with fluctuations in on-site skin conductance response (SCR). In the second stage, laboratory experiments were conducted while participants viewed the selected images, during which eye-tracking, skin conductance, and heart rate data were recorded simultaneously. These measures were used to characterize visual attention allocation and autonomic physiological responses across different rural landscape types, rather than to directly measure landscape preference. For Area of Interest (AOI) analysis, each image was coded into six landscape element categories: vegetation, buildings, roads, sky, vernacular buildings, and water bodies. The results revealed significant typological differences in overall visual search patterns and autonomic responses. Gaze hotspots were concentrated on identifiable targets and boundary regions in the foreground and midground, whereas the sky attracted relatively limited attention. FF primarily emphasized vernacular buildings and farmland boundaries, HF emphasized settlement interfaces and spatial transition nodes, DP emphasized road junctions and facilities along routes, and WNL emphasized water bodies and water–land interface zones. These findings suggest that a two-stage multimodal design can provide supporting evidence for understanding type-specific perceptual responses and can support more targeted strategies for rural landscape renewal. Full article

Background/Objectives: Digital systems are being increasingly used to mediate pediatric care, yet many competency models remain predominantly technical and may unintentionally dilute patient- and family-centered care. This study aimed to identify empirically grounded digital competencies that enable pediatric nurse leaders to sustain patient- and family-centered care and to propose a practice-ready competency map. Methods: An interpretative phenomenological study was conducted across three hospitals in Saudi Arabia, purposively selected for varying levels of digital maturity. Ten pediatric nurse leaders completed two in-depth, semi-structured interviews (60–90 min) and a four-week reflective journal. Data were analyzed ideographically and then across cases using interpretative phenomenological analysis. Interviews were conducted in Arabic or English; translation included professional translation, partial back-translation (30%), and bilingual review. Results: Four interlinked competency domains emerged: (1) Relational digital presence, co-viewing the electronic health record, narrating documentation, and coordinating gaze and screen use to preserve relational connection; (2) Vulnerable expertise, micro-coaching at the point of care and transparent discussion of near-misses to build psychological safety; (3) Culturally legible communication, multimodal, language-congruent communication and explicit boundaries for sensitive information; and (4) Judgment-with-data, documenting override rationales and balancing algorithmic indicators with contextual family need. Together, these domains formed a screen-side competency map translating lived experience into trainable micro-practices. Conclusions: Digital competence in pediatric nursing leadership is relational, culturally situated, and clinically interpretive rather than a linear technical checklist. Embedding these competencies into leadership development and digital workflow design may help protect and strengthen patient- and family-centered care in technology-mediated pediatric care. Full article

With the growing commercial prominence of virtual idol livestreaming, this study examines how gaze strategies employed by virtual idols in livestreaming contexts are associated with viewers’ online interaction intention and tests the mediating roles of social presence and trust. Drawing on the SOR model, this study conceptualizes gaze strategies through a two-layer stimulus structure that integrates virtual idol behavioral cues and viewers’ perceptual responses. A 3 × 3 experimental design was employed, manipulating gaze intensity and gaze dynamics at the behavioral layer using virtual idol livestream clips as stimuli, while participants’ perceived gaze was treated as a stimulus variable at the perceptual layer. Data from 398 participants were analyzed using partial least squares structural equation modelling and multivariate analysis of variance. Results indicate that gaze intensity is positively associated with perceived gaze, which in turn is linked to higher levels of social presence and trust. By contrast, the overall effect of gaze dynamics appears more limited, although high-dynamics conditions are associated with lower levels of trust and online interaction intention. The structural model provides evidence that gaze strategies are indirectly associated with online interaction intention through the mediating roles of social presence and trust. The contributions of this study are twofold. First, it provides an empirical basis for subsequent research on virtual character behavior in livestreaming contexts. Second, it offers context-specific insight into a potential pathway through which gaze-related cues may be associated with online interaction intention. Full article

This study investigates how YouTube food travel vloggers semiotically construct destination images and potentially function as informal culinary ambassadors through gastrodiplomacy mechanisms, using Uzbekistan as a case study of emerging tourism markets. Although digital content creators are increasingly recognised as shaping tourism flows, a systematic understanding of the multimodal semiotic mechanisms through which food travel vlogs construct destination meanings remains limited. Using multimodal discourse analysis, this study examines six YouTube food travel videos on Uzbekistan (over 28 million combined views) from two prominent creators. The analysis integrates Kress and van Leeuwen’s visual grammar, Halliday’s systemic functional linguistics, van Leeuwen’s sound semiotics, and Norris’s multimodal interaction analysis to code a 60-segment corpus. Comparative analysis reveals 25 notable differences in semiotic features between the two creators, identifying two distinct semiotic profiles. Vlogger 1 primarily follows a parasocial intimacy model marked by direct gaze (89.2%), frequent second-person address (78.4%), and comparatively minimal editing. In contrast, Vlogger 2 adopts a cinematic documentary model characterised by first-person narration (56.5%), constructed visuals (60.9%), and gastronomic heritage narratives (34.8%). Despite these divergences, shared conventions centred on food composition, upbeat music, positive evaluation, and sharing gestures indicate a stable semiotic grammar of food travel vlogging. Analysis further reveals that orientalist dynamics and resistance to orientalism coexist within the same representational practice phenomenon termed ‘layered orientalism’, with distinct implications for how emerging destinations are mediated to international audiences. These findings suggest that digital content creators may employ distinct semiotic strategies that could function as informal culinary ambassadors through gastrodiplomacy mechanisms, potentially constructing destination awareness and cultural meaning for international audiences. This study contributes to theory on multimodal destination image construction and offers implications for how emerging tourism destinations might leverage multi-creator strategies to build culturally grounded destination brands. Full article

This study explores the use of transfer learning in gaze estimation, focusing on the development of personalized models tailored to individual users. Our approach involves collecting gaze data using standard laptop webcams, designed to operate effectively within resource-limited settings, thereby enhancing accessibility and affordability. We conducted a comparative analysis of models using transfer learning against models trained without pre-trained weights, examining their convergence behavior and sensitivity to different dataset sizes. The analysis includes both eye and face images, providing a comprehensive view of model adaptability. Our findings show that while both methods produce comparable results overall, transfer learning offers notable advantages—particularly faster convergence, reduced computational cost, and enhanced stability when data are limited. However, the results also reveal that transfer learning is not universally superior; for face images, models trained from scratch achieved lower mean errors but exhibited higher variability, whereas transfer learning ensured more consistent performance. These insights highlight that the benefits of transfer learning depend on the data characteristics and task complexity. In the most data-constrained setting (100 images), transfer learning reduced the mean error by 20.99 px for the Left Eye model and 35.56 px for the Right Eye model, whereas for face images the models trained from scratch consistently achieved lower mean error across all evaluated dataset sizes. Overall, this study underscores the potential of transfer learning for efficient and scalable gaze estimation, particularly in small-data environments, while providing practical guidance on when and how transfer learning yields the greatest benefit for real-time applications such as human–computer interaction (HCI), assistive technologies, and personalized user experiences. Full article

Secure coding is essential, yet the strategies developers use to detect and mitigate flaws are not well understood. We present an eye-tracking-based approach that captures developers’ visual patterns while reading, coding, and applying security tools. Our framework uses participant-editable stimuli and dynamic environments to reflect authentic coding development. By visualizing gaze transitions and attention shifts, we expose how developers allocate effort during secure coding. By leveraging techniques that reveal gaze transitions, attention levels, and pupil size changes, we are able to gain insight into their behavior. Our study provides a fine-grained, process-oriented account of behavior in CWE-based secure coding educational tasks, uncovering attentional patterns and decision timelines that traditional methods may not capture. These contributions provide a foundation for improving training and understanding developer differences. Full article