Search Results (557)

The formation mechanism of Virtual Reality (VR) Immersive Experience (VRIE) is notably complex; this study aimed to dynamically decode its underlying drivers by innovatively integrating Flow Theory and Privacy Calculus Theory, focusing on Perceptual-Interactive Fidelity (PIF), Consumer Willingness to Immerse in Technology (CWTI), and the applicability of Loss Aversion Theory. To achieve this, we analyzed approximately 30,000 user reviews from Amazon using Latent Semantic Analysis (LSA) and regression analysis. The findings reveal that user attention’s impact on VRIE is non-linear, suggesting an optimal threshold, and confirm PIF as a central influencing mechanism; furthermore, CWTI significantly moderates users’ privacy calculus, thereby affecting VRIE, while Loss Aversion Theory showed limited explanatory power in the VR context. These results provide a deeper understanding of VR user behavior, offering significant theoretical guidance and practical implications for future VR system design, particularly in strategically balancing user cognition, PIF, privacy concerns, and individual willingness. Full article

Advanced three-dimensional extended reality (XR) technologies are highly suitable for cultural heritage research and education. XR tools enable the creation of realistic virtual or augmented reality applications for curating and disseminating information about cultural artifacts and sites. Developing XR applications for cultural heritage requires interdisciplinary collaboration involving strong teamwork and soft skills to manage user requirements, system specifications, and design cycles. Given the diverse end-users, achieving high precision, accuracy, and efficiency in information management and user experience is crucial. Human–computer interaction (HCI) design and evaluation methods are essential for ensuring usability and return on investment. This article presents ten case studies of cultural heritage software projects, illustrating the interdisciplinary work between computer science and HCI design. Students from institutions such as the State University of New York (USA), Glasgow School of Art (UK), University of Granada (Spain), University of Málaga (Spain), Duy Tan University (Vietnam), Imperial College London (UK), Research University Institute of Communication & Computer Systems (Greece), Technical University of Košice (Slovakia), and Indiana University (USA) contributed to creating, assessing, and improving the usability of these diverse cultural heritage applications. The results include a structured typology of CH XR application scenarios, detailed insights into design and evaluation practices across ten international use cases, and a development framework that supports interdisciplinary collaboration and stakeholder integration in phygital cultural heritage projects. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. Full article

Collaborative digital games and interfaces are increasingly used in both research and commercial contexts, yet little is known about how the spatial arrangement and interface sharing affect the user experience in dyadic settings. Using a two-player iPad pong game, this study compared user experiences across three collaborative gaming scenarios: face-to-face single-screen (F2F-OneS), face-to-face dual-screen (F2F-DualS), and remote dual-screen (Rmt-DualS) scenarios. Eleven dyads participated in all conditions using a within-subject design. After each session, the participants completed a 21-item user experience questionnaire and took part in brief interviews. The results from a repeated-measure ANOVA and post hoc paired t-tests showed significant scenario effects for several experience items, with F2F-OneS yielding higher engagement, novelty, and accomplishment than remote play, and qualitative interviews supported the quantitative findings, revealing themes of social presence and interaction. These results highlight the importance of spatial and interface design in collaborative settings, suggesting that both technical and social factors should be considered in multi-user interface development. Full article

Human–computer interaction (HCI) plays a crucial role across various fields, with eye-tracking technology emerging as a key enabler for intuitive and dynamic control in assistive systems like Assistive Robotic Arms (ARAs). By precisely tracking eye movements, this technology allows for more natural user interaction. However, current systems primarily rely on the single gaze-dependent interaction method, which leads to the “Midas Touch” problem. This highlights the need for real-time eye movement classification in dynamic interactions to ensure accurate and efficient control. This paper proposes a novel Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) classification algorithm aimed at overcoming the limitations of traditional methods in dynamic human–robot interactions. By incorporating sum of squared error (SSE)-based feature extraction and hierarchical training, the proposed algorithm achieves a classification accuracy of 94.39%, significantly outperforming existing approaches. Furthermore, it is integrated with a robotic arm system, enabling gaze trajectory-based dynamic path planning, which reduces the average path planning time to 2.97 milliseconds. The experimental results demonstrate the effectiveness of this approach, offering an efficient and intuitive solution for human–robot interaction in dynamic environments. This work provides a robust framework for future assistive robotic systems, improving interaction intuitiveness and efficiency in complex real-world scenarios. Full article

Understanding how cognitive differences shape visitor behavior in digital heritage experiences is essential for designing inclusive and engaging museum technologies. This study explores the relationship between cognitive level and interaction behavior, affective responses, and sensor-based engagement using a publicly available dataset from a digital museum game. Participants (N = 1000) were categorized into three cognitive levels (Early, Developing, and Advanced), and their data were analyzed across three domains: user interaction behavior, affective and performance states, and sensor-based interaction measures. Our findings suggest that sensor-level interactions are more sensitive indicators of cognitive differences than observable behavior or inferred affect. This work contributes to the heritage HCI field by highlighting the potential for cognitively adaptive systems that personalize the museum experience in real-time, enhancing accessibility, engagement, and learning in cultural settings. Full article

Remote photoplethysmography (rPPG) enables non-contact physiological measurement for emotion recognition, yet the temporally sparse nature of emotional cardiovascular responses, intrinsic measurement noise, weak session-level labels, and subtle correlates of valence pose critical challenges. To address these issues, we propose a physiologically inspired deep learning framework comprising a Multi-scale Temporal Dynamics Encoder (MTDE) to capture autonomic nervous system dynamics across multiple timescales, an adaptive sparse α-Entmax attention mechanism to identify salient emotional segments amidst noisy signals, Gated Temporal Pooling for the robust aggregation of emotional features, and a structured three-phase curriculum learning strategy to systematically handle temporal sparsity, weak labels, and noise. Evaluated on the MAHNOB-HCI dataset (27 subjects and 527 sessions with a subject-mixed split), our temporal-only model achieved competitive performance in arousal recognition (66.04% accuracy; 61.97% weighted F1-score), surpassing prior CNN-LSTM baselines. However, lower performance in valence (62.26% accuracy) revealed inherent physiological limitations regarding a unimodal temporal cardiovascular analysis. These findings establish clear benchmarks for temporal-only rPPG emotion recognition and underscore the necessity of incorporating spatial or multimodal information to effectively capture nuanced emotional dimensions such as valence, guiding future research directions in affective computing. Full article

Integration of advanced artificial intelligence with neurotechnology offers transformative potential for assistive communication. This perspective article examines the emerging convergence between non-invasive brain–computer interface (BCI) spellers and large language models (LLMs), with a focus on predictive communication for individuals with motor or language impairments. First, I will review the evolution of language models—from early rule-based systems to contemporary deep learning architectures—and their role in enhancing predictive writing. Second, I will survey existing implementations of BCI spellers that incorporate language modeling and highlight recent pilot studies exploring the integration of LLMs into BCI. Third, I will examine how, despite advancements in typing speed, accuracy, and user adaptability, the fusion of LLMs and BCI spellers still faces key challenges such as real-time processing, robustness to noise, and the integration of neural decoding outputs with probabilistic language generation frameworks. Finally, I will discuss how fully integrating LLMs with BCI technology could substantially improve the speed and usability of BCI-mediated communication, offering a path toward more intuitive, adaptive, and effective neurotechnological solutions for both clinical and non-clinical users. Full article

This study systematically investigates the mechanism by which total ionizing dose (TID) affects the lifetime degradation of NMOS devices induced by hot-carrier injection (HCI). Experiments involved Cobalt-60 (Co-60) gamma-ray irradiation to a cumulative dose of 500 krad (Si), followed by 168 h annealing at 100 °C to simulate long-term stability. However, under HCI stress conditions (V_D = 2.7 V, V_G = 1.8 V), irradiated devices show a 6.93% increase in threshold voltage shift (ΔV_th) compared to non-irradiated counterparts. According to the IEC 62416 standard, the lifetime degradation of irradiated devices induced by HCI stress is only 65% of that of non-irradiated devices. Conversely, when the saturation drain current (I_Dsat) degrades by 10%, the lifetime doubles compared to non-irradiated counterparts. Mechanistic analysis demonstrates that partial neutralization of E’ center positive charges at the gate oxide interface by hot electrons weakens the electric field shielding effect, accelerating ΔV_th drift, while interface trap charges contribute minimally to degradation due to annealing-induced self-healing. The saturation drain current shift degradation primarily correlates with electron mobility variations. This work elucidates the multi-physics mechanisms through which TID impacts device reliability and provides critical insights for radiation-hardened design optimization. Full article

This study investigates the biotribological performance of alumina–UHMWPE and alumina–PEEK hip implant couples through finite element simulation (ANSYS v24) and statistical inference (STATA v17). During gait cycle loading simulations, significant disparity in wear behaviour was observed. Alumina–UHMWPE demonstrated superior mechanical resistance, with a wear volume of 0.18481 mm³ and a wear depth of 6.93 × 10⁻⁴ mm compared to alumina–PEEK, which registered higher wear (volume: 8.4006 mm³; depth: 3.15 × 10⁻² mm). Wear distribution analysis indicated alumina–UHMWPE showed an even wear pattern in comparison to the poor, uneven alumina-PEEK high-wear patterns. Statistical comparison validated these findings, wherein alumina–UHMWPE achieved a 27.60 hip joint wear index (HCI) value, which is better than that of alumina–PEEK (35.85 HCI), particularly regarding key parameters like wear depth and volume. This computational–statistical model yields a baseline design for biomaterial choice, demonstrating the potential clinical superiority of alumina–UHMWPE in reducing implant failure risk. While this is a simulation study lacking experimental validation, the results pave the way for experimental and clinical studies for further verification and refinement. The approach enables hip arthroplasty design optimization with maximal efficiency and minimal resource-intensive testing. Full article

Speech emotion recognition (SER) plays a vital role in enhancing human–computer interaction (HCI) and can be applied in affective computing, virtual support, and healthcare. This research presents a high-performance SER framework based on a lightweight 1D Convolutional Neural Network (1D-CNN) and a multi-feature fusion technique. Rather than employing spectrograms as image-based input, frame-level characteristics (Mel-Frequency Cepstral Coefficients, Mel-Spectrograms, and Chroma vectors) are calculated throughout the sequences to preserve temporal information and reduce the computing expense. The model attained classification accuracies of 94.0% on MELD (multi-party talks) and 91.9% on RAVDESS (acted speech). Ablation experiments demonstrate that the integration of complimentary features significantly outperforms the utilisation of a singular feature as a baseline. Data augmentation techniques, including Gaussian noise and time shifting, enhance model generalisation. The proposed method demonstrates significant potential for real-time emotion recognition using audio only in embedded or resource-constrained devices. Full article

The aim of this article is to critically and reliably assess the potential of current emotion recognition technologies for practical applications in human–computer interaction (HCI) systems. The study made use of two databases: one in English (RAVDESS) and another in Polish (EMO-BAJKA), both containing speech recordings expressing various emotions. The effectiveness of recognizing seven and eight different emotions was analyzed. A range of acoustic features, including energy features, mel-cepstral features, zero-crossing rate, fundamental frequency, and spectral features, were utilized to analyze the emotions in speech. Machine learning techniques such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and support vector machines with a cubic kernel (cubic SVMs) were employed in the emotion classification task. The research findings indicated that the effective recognition of a broad spectrum of emotions in a subject-independent approach is limited. However, significantly better results were obtained in the classification of paired emotions, suggesting that emotion recognition technologies could be effectively used in specific applications where distinguishing between two particular emotional states is essential. To ensure a reliable and accurate assessment of the emotion recognition system, care was taken to divide the dataset in such a way that the training and testing data contained recordings of completely different individuals. The highest classification accuracies for pairs of emotions were achieved for Angry–Fearful (0.8), Angry–Happy (0.86), Angry–Neutral (1.0), Angry–Sad (1.0), Angry–Surprise (0.89), Disgust–Neutral (0.91), and Disgust–Sad (0.96) in the RAVDESS. In the EMO-BAJKA database, the highest classification accuracies for pairs of emotions were for Joy–Neutral (0.91), Surprise–Neutral (0.80), Surprise–Fear (0.91), and Neutral–Fear (0.91). Full article

The isotopic shift of the bound-electron g factor in highly charged ions (HCI) provides a sensitive probe for testing physics beyond the Standard Model, particularly through interactions mediated by a hypothetical scalar boson. In this study, we analyze the sensitivity of this method within the Higgs portal framework, focusing on the uncertainties introduced by quantum electrodynamics corrections, including finite nuclear size, nuclear recoil, and nuclear polarization effects. All calculations are performed for the ground-state $1s$ configuration of hydrogen-like HCI, where theoretical predictions are most accurate. Using selected isotope pairs (e.g., ${\mathrm{He}}^{4/6}$, ${\mathrm{Ne}}^{20/22}$, ${\mathrm{Ca}}^{40/48}$, ${\mathrm{Sn}}^{120/132}$, ${\mathrm{Th}}^{230/232}$), we demonstrate that the dominant source of uncertainty arises from finite nuclear size corrections, which currently limit the precision of new physics searches. Our results indicate that the sensitivity of this method decreases with increasing atomic number. These findings highlight the necessity of improved nuclear radius measurements and the development of alternative approaches, such as the special differences method, to enable virtually the detection of fifth-force interactions. Full article

Language is that kind of expression by which effective communication with another can be well expressed. One may consider such as a connecting bridge for bridging communication gaps for the hearing- and speech-impaired, even though it remains as an advanced method for hand gesture expression along with identification through the various different unidentified signals to configure their palms. This challenge can be met with a novel Enhanced Convolutional Transformer with Adaptive Tuna Swarm Optimization (ECT-ATSO) recognition framework proposed for double-handed sign language. In order to improve both model generalization and image quality, preprocessing is applied to images prior to prediction, and the proposed dataset is organized to handle multiple dynamic words. Feature graining is employed to obtain local features, and the ViT transformer architecture is then utilized to capture global features from the preprocessed images. After concatenation, this generates a feature map that is then divided into various words using an Inverted Residual Feed-Forward Network (IRFFN). Using the Tuna Swarm Optimization (TSO) algorithm in its enhanced form, the provided Enhanced Convolutional Transformer (ECT) model is optimally tuned to handle the problem dimensions with convergence problem parameters. In order to solve local optimization constraints when adjusting the position for the tuna update process, a mutation operator was introduced. The dataset visualization that demonstrates the best effectiveness compared to alternative cutting-edge methods, recognition accuracy, and convergences serves as a means to measure performance of this suggested framework. Full article

The research into human–canine interactions (HCIs) has grown substantially, yet limited attention has focused on the welfare of canines involved, particularly pet dogs owned by volunteer participants. To address this gap, we conducted a secondary analysis of data from a randomized controlled trial, examining canine welfare during an acute human stress protocol. Our methodology incorporated evidence-based screening tools, environmental modifications, researchers trained in canine behavior assessments and safe interactions, and canine stress monitoring using the Fear Free™ Canine Fear, Anxiety, and Stress (FAS) Spectrum. Dogs’ stress levels showed a non-significant increase from the rest to stressor phase (0.80 to 1.00, p = 0.073) and a significant decrease during recovery (1.00 to 0.48, p < 0.001). Only two dogs (7.6%) required withdrawal due to elevated stress levels, though these levels remained within acceptable safety parameters. The peak stress remained within acceptable limits, with only 24% (6 of 25) reaching an FAS score of two during the TSST. By final recovery, 96% of dogs achieved FAS scores of zero to one (Green Zone), indicating relaxed states. Salivary collection proved challenging, highlighting limitations in low-invasive physiological measurement techniques. Based on our findings and literature review, we propose standardized guidelines for HCI research, including thorough pre-screening, environmental preparation, researcher training, stress-monitoring protocols, and informed consent procedures emphasizing withdrawal rights. These guidelines aim to establish ethical standards for this rapidly expanding field, protecting canine participant welfare while enabling valuable research to continue. Full article