Search Results (235)

Background/Objectives: Mental health and emotional well-being are critical yet often overlooked in older adults. Immersive virtual reality (VR) may offer novel strategies for promoting relaxation in this population, but the diversity of intervention designs and underlying mechanisms remains insufficiently mapped. This scoping review aimed to systematically examine head-mounted display (HMD) VR interventions for relaxation in older adults, focusing on core scenarios, mechanisms, and conceptual paradigms. Methods: Following the JBI Manual for Evidence Synthesis and PRISMA-ScR guidelines, comprehensive searches of MEDLINE, Cochrane Library, Web of Science, and Scopus were conducted for empirical studies of HMD-based VR relaxation interventions in adults aged 60 and older. Data on study characteristics, hardware, intervention scenarios, and reported physiological and psychological mechanisms were extracted and categorized into conceptual paradigms. Results: Twenty-four studies were included, covering diverse clinical and community samples. Four main VR relaxation paradigms emerged: environmental relaxation, guided meditation, psychotherapy integration, and interactive gamified relaxation. Reported mechanisms included parasympathetic activation, multisensory immersion, attentional distraction, positive affect induction, mindfulness, cognitive engagement, and reminiscence activation. Considerable heterogeneity was found in intervention design and outcomes, with mechanisms often inferred rather than directly described. Conclusions: Similar relaxation mechanisms were engaged across paradigms, including hybrid interventions combining multiple approaches, highlighting the adaptability of VR to meet diverse needs. Identifying these four paradigms provides a foundation for future research and development of targeted VR-based relaxation for older adults. Further studies should clarify mechanisms, use standardized physiological outcome measures, and assess long-term benefits of VR relaxation interventions in geriatric populations. Full article

This study examined how digital technology facilitated early childhood music learning in multi-sensory, engaging experiences. In a 16-week quasi-experimental, mixed-method study that used the Holistic Music Educational Approach for Young Children (HMEAYC) with 103 children and 36 pre-service teachers in Taiwan, sensor-based audio devices and responsive technologies were used instead of screens. Observations and video analysis showed that after an initial phase of adaptation, children exhibited growth in spontaneous and imitative musical behaviors, sensory integration, motor coordination, and creativity. Full article

This paper presents the design and evaluation of a multisensory tangible game device aimed at promoting pre-Braille literacy in children with varying visual abilities, including those who are blind, partially sighted, and sighted. The prototype integrates tactile, auditory, and visual elements to provide an inclusive and engaging learning experience. The device combines educational content with game-based learning, allowing users to interact with Braille characters through touch while receiving auditory feedback and visual cues. A focus group evaluation was conducted to assess the prototype’s effectiveness, engagement, and educational value. Results indicated that the device successfully captured users’ attention, with 83% recognizing its potential as a valuable educational tool for teaching pre-Braille literacy, 92% of participants reporting high engagement, and 75% of participants agreeing with the serious game approach. Feedback also highlighted areas for improvement, including the need for clearer tactile differentiation and more adaptive learning features. This study demonstrates the potential of combining multisensory feedback and serious gaming to enhance literacy education in children with visual impairments and provides insights into the further development of inclusive educational technologies. Full article

This study focuses on the mechanisms of behavior and cognition, providing a comprehensive analysis of the innovative path of multimodal learning theory in the teaching practice of the “Web Front-end Development” course. This study integrates different sensory modes, such as vision, hearing, and haptic feedback, with the core objective of exploring the specific impact of this multi-sensory integration form on students’ cognitive engagement status, classroom interaction styles, and long-term learning behavior. We employed a mixed-methods approach in this study. On the one hand, we conducted a quasi-experiment involving 120 undergraduate students. On the other hand, research methods such as behavioral coding, in-depth interviews, and longitudinal tracking were also employed. Results show that multimodal teaching significantly reduces cognitive load (a 34.9% reduction measured by NASA-TLX), increases the frequency of collaborative interactions (2.3 times per class), and extends voluntary practice time (8.5 h per week). Mechanistically, these effects are mediated by enhanced embodied cognition (strengthening motor-sensory memory), optimized cognitive load distribution (reducing extraneous mental effort), and the fulfillment of intrinsic motivational needs (autonomy, competence, relatedness) as framed by self-determination theory. This study fills in the gap between educational technology and behavioral science. We have developed a comprehensive framework that provides practical guidance for designing technology-enhanced learning environments. With such a framework, learners can not only master technical skills more smoothly but also maintain their enthusiasm for learning for a long time and continue to participate. Full article

As mental health issues such as stress, anxiety, and depression become increasingly prevalent in urban populations, there is a critical need to embed restorative functions into the built environment. Urban parks, as integral components of ecological infrastructure, play a vital role in promoting psychological well-being. This study explores how diverse park environments facilitate mental health recovery through multi-sensory engagement, using integrated psychophysiological assessments in a wetland park in Zhengzhou, China. Electroencephalography (EEG) and perceived restoration scores were employed to evaluate recovery outcomes across four environmental types: waterfront, wetland, forest, and plaza. Key perceptual factors—including landscape design, spatial configuration, biodiversity, and facility quality—were validated and analyzed for their roles in shaping restorative experiences. Results reveal significant variation in recovery effectiveness across environments. Waterfront areas elicited the strongest physiological responses, while plazas demonstrated lower restorative benefits. Two recovery pathways were identified: a direct, sensory-driven process and a cognitively mediated route. Biodiversity promoted physiological restoration only when mediated by perceived restorative qualities, whereas landscape and spatial attributes produced more immediate effects. Facilities supported psychological recovery mainly through cognitive appraisal. The study proposes a smart park framework that incorporates environmental sensors, adaptive lighting, real-time biofeedback systems, and interactive interfaces to enhance user engagement and monitor well-being. These technologies enable urban parks to function as intelligent, health-supportive infrastructures within the broader built environment. The findings offer evidence-based guidance for designing responsive green spaces that contribute to mental resilience, aligning with the goals of smart city development and healthy life-building environments. Full article

This paper addresses the problem of recognising and classifying hand movements to control an upper-limb exoskeleton. To solve this problem, a multisensory system based on the fusion of data from electromyography (EMG) sensors, inertial measurement units (IMUs), and virtual reality (VR) trackers is proposed, which provides highly accurate detection of users’ movements. Signal preprocessing (noise filtering, segmentation, normalisation) and feature extraction were performed to generate input data for regression and classification models. Various machine learning algorithms are used to recognise motor activity, ranging from classical algorithms (logistic regression, k-nearest neighbors, decision trees) and ensemble methods (random forest, AdaBoost, eXtreme Gradient Boosting, stacking, voting) to deep neural networks, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformers. The algorithm for integrating machine learning models into the exoskeleton control system is considered. In experiments aimed at abandoning proprietary tracking systems (VR trackers), absolute position regression was performed using data from IMU sensors with 14 regression algorithms: The random forest ensemble provided the best accuracy (mean absolute error = 0.0022 metres). The task of classifying activity categories out of nine types is considered below. Ablation analysis showed that IMU and VR trackers produce a sufficient informative minimum, while adding EMG also introduces noise, which degrades the performance of simpler models but is successfully compensated for by deep networks. In the classification task using all signals, the maximum result (99.2%) was obtained on Transformer; the fully connected neural network generated slightly worse results (98.4%). When using only IMU data, fully connected neural network, Transformer, and CNN–GRU networks provide 100% accuracy. Experimental results confirm the effectiveness of the proposed architectures for motor activity classification, as well as the use of a multi-sensor approach that allows one to compensate for the limitations of individual types of sensors. The obtained results make it possible to continue research in this direction towards the creation of control systems for upper exoskeletons, including those used in rehabilitation and virtual simulation systems. Full article

This paper presents a bionic extended Kalman filter (EKF) state estimation algorithm for agricultural planters, inspired by the bionic mechanism of migratory birds navigating in complex environments, where migratory birds achieve precise localization behaviors by fusing multi-sensory information (e.g., geomagnetic field, visual landmarks, and somatosensory balance). The algorithm mimics the migratory bird’s ability to integrate multimodal information by fusing laser SLAM, inertial measurement unit (IMU), and GPS data to estimate the position, velocity, and attitude of the planter in real time. Adopting a nonlinear processing approach, the EKF effectively handles nonlinear dynamic characteristics in complex terrain, similar to the adaptive response of a biological nervous system to environmental perturbations. The algorithm demonstrates bio-inspired robustness through the derivation of the nonlinear dynamic teaching model and measurement model and is able to provide high-precision state estimation in complex environments such as mountainous or hilly terrain. Simulation results show that the algorithm significantly improves the navigation accuracy of the planter in unstructured environments. A new method of bio-inspired adaptive state estimation is provided. Full article

The shift toward experience-oriented travel has positioned food as a central driver for attracting visitors to sustainable destinations, directly supporting United Nations Sustainable Development Goal (SDG)11 (resilient cities) and SDG 12 (responsible consumption). While prior research has predominantly emphasised marketing outcomes, the role of bodily experiences in shaping gastronomic tourism has received less attention. This study explores how sensory elements (sight, sound, smell, taste, and touch) and non-sensory elements (including cultural meaning and service quality) jointly influence food-related travel experiences. Twenty-five self-identified food travellers were interviewed in a United Nations Educational, Scientific and Cultural Organization (UNESCO) City of Gastronomy, and their narratives were analysed using a three-stage grounded theory approach in NVivo 12. The resulting model identifies four interrelated dimensions: (1) embodied experience, grounded in culinary memories and shared cultural narratives; (2) sensory stimulation arising from food and its surroundings; (3) situated embodiment, shaped by location, timing, and social interaction; and (4) environmental perception, encompassing food presentation, facility quality, cleanliness, and pricing fairness. These dimensions interact to enhance overall experience quality. By integrating an embodied perspective with a sustainability focus, this study advances tourism experience research and offers practical guidance for designing multisensory dining environments, fostering environmentally responsible visitor behaviour, and ensuring a balanced relationship between price and perceived value. Full article

From the moment we are born, and even before, in the womb, and until our last breath, our bodies move all the time. Adaptive behaviors necessarily depend not only on the successful integration of multisensory bodily signals but also on how we move our bodies in the world. This paper considers the notion of embodied selfhood through the perspective of dynamic and rhymical coupling between bodily movements and bodily actions. We propose a new theoretical framework suggesting that the dynamic coupling between bodily movements and bodily actions in the world are fundamental in constructing and maintaining a coherent sense of self. To support this idea, we use the Predictive Processing (PP) and Active Inference frameworks as our background theoretical canvas. Specifically, we will focus on the phenomenon of somatosensory attenuation in relation to dynamic selfhood and argue that rhythmic bodily signals such as heartbeats, breathing, and walking patterns are predictable and, thus, can be smoothly attenuated, i.e., processed in the background. We illustrate this hypothesis by discussing the case of Depersonalization Disorder as a failure to self-attenuate self-related information processing, leading to feelings of unreality and self ‘loss’. We conclude with potential implications of our hypothesis for therapy. Full article

Children’s physical and mental wellbeing is declining, partly due to reduced independent mobility and lack of engaging public environments. This study explores a co-design approach in which children actively participated in a series of design workshops focused on improving school routes through sensory engagement and imagination. Using sensory walks, students mapped positive and negative experiences in their everyday surroundings. Through hands-on creative exercises and the integration of AI and VR tools, they developed design proposals envisioning safer, more enjoyable, and inclusive mobility environments. The findings reveal that while children are highly capable of generating creative and context-sensitive ideas, they are less accustomed to reflecting on sensory input beyond vision. The results underscore the importance of designing urban spaces that prioritize safety, playfulness, and multisensory richness, with particular emphasis on nature and emotional connection. Full article

Art therapy serves as a crucial intervention modality for children with autism spectrum disorder (ASD), demonstrating unique value in emotional expression, sensory integration, and social communication. However, current practice presents critical challenges, including the disconnect between design expertise and clinical needs, unclear mechanisms of environmental factors’ impact on therapeutic outcomes, and insufficient evidence-based support for technology integration. Purpose: This study aimed to construct an evidence-based theoretical framework for art therapy environment design for children with autism, clarifying the relationship between environmental design elements and therapeutic effectiveness. Methodology: Based on the Web of Science database, this study employed a dual-track approach comprising bibliometric analysis and micro-qualitative content analysis to systematically examine the knowledge structure and developmental trends. Research hotspots were identified through keyword co-occurrence network analysis using CiteSpace, while 24 representative design cases were analyzed to gain insights into design concepts, emerging technologies, and implementation principles. Key Findings: Through keyword network visualization analysis, this study identified ten primary research clusters that were systematically categorized into four core design elements: sensory feedback design, behavioral guidance design, emotional resonance design, and therapeutic support design. A responsive therapeutic environment conceptual framework was proposed, encompassing four interconnected components based on the ABC model from positive psychology: emotional, sensory, environmental, and behavioral dimensions. Evidence-based design principles were established emphasizing child-centeredness, the promotion of multisensory expression, the achievement of dynamic feedback, and appropriate technology integration. Research Contribution: This research establishes theoretical connections between environmental design elements and art therapy effectiveness, providing a systematic design guidance framework for interdisciplinary teams, including environmental designers, clinical practitioners, technology developers, and healthcare administrators. The framework positions technology as a therapeutic mediator rather than a driver, ensuring technological integration supports rather than interferes with children’s natural creative impulses. This contributes to creating more effective environmental spaces for art therapy activities for children with autism while aligning with SDG3 goals for promoting mental health and reducing inequalities in therapeutic access. Full article

Multisensory integration is crucial for effective cognitive functioning, especially in complex tasks such as those requiring rapid audiovisual information processing. High-level martial arts routine athletes, trained in integrating visual and auditory cues for performance, may exhibit superior abilities in cross-audiovisual integration. This study aimed to explore whether these athletes demonstrate an expert advantage effect in audiovisual integration, particularly focusing on whether this advantage is due to enhanced automatic auditory processing. A total of 165 participants (81 male, 84 female) were included in three experiments. Experiment 1 (n = 63) used a cross-audiovisual Rapid Serial Visual Presentation (RSVP) paradigm to compare the martial arts routine athlete group (n = 31) with a control group (n = 33) in tasks requiring target stimulus identification under audiovisual congruent and incongruent conditions. Experiment 2 (n = 52) manipulated the synchronicity of auditory stimuli to differentiate between audiovisual integration and auditory alerting effects. Experiment 3 (n = 50) combined surprise and post-surprise tests to investigate the role of automatic auditory processing in this expert advantage. Experiment 1 revealed that martial arts routine athletes outperformed the control group, especially in semantically incongruent conditions, with significantly higher accuracy at both lag3 (p < 0.001, 95% CI = [0.165, 0.275]) and lag8 (p < 0.001, 95% CI = [0.242, 0.435]). Experiment 2 found no significant difference between groups in response to the manipulation of auditory stimulus synchronicity, ruling out an alerting effect. In Experiment 3, martial arts routine athletes demonstrated better performance in reporting unexpected auditory stimuli during the surprise test, indicating enhanced automatic processing capacity. Additionally, a significant improvement in working memory re-selection was observed in the martial arts routine group. The expert advantage effect observed in martial arts routine athletes is attributable to enhanced cross-audiovisual integration, independent of an auditory alerting mechanism. Long-term training improves the efficiency of working memory re-selection and the ability to inhibit conflicting information, suggesting that the expanded capacity for automatic auditory processing underpins their multisensory integration advantage. Full article

As autonomous driving technologies advance, the need for human-in-the-loop systems becomes increasingly critical to ensure safety, adaptability, and public confidence. This paper presents the design and evaluation of a context-aware immersive teleoperation interface that integrates real-time simulation, virtual reality, and multimodal feedback to support remote interventions in emergency scenarios. Built on a modular ROS2 architecture, the system allows seamless transition between simulated and physical platforms, enabling safe and reproducible testing. The experimental results show a high task success rate and user satisfaction, highlighting the importance of intuitive controls, gesture recognition accuracy, and low-latency feedback. Our findings contribute to the understanding of human-robot interaction (HRI) in immersive teleoperation contexts and provide insights into the role of multisensory feedback and control modalities in building trust and situational awareness for remote operators. Ultimately, this approach is intended to support the broader acceptability of autonomous driving technologies by enhancing human supervision, control, and confidence. Full article

Immersive technologies such as augmented reality (AR) and virtual reality (VR) have emerged as powerful tools in cultural heritage education and preservation. Building on prior work that demonstrated the effectiveness of gamified XR applications in engaging users with heritage content and drawing on existing studies in neuroscience and cognitive psychology, this study explores how immersive experiences support multisensory integration, emotional engagement, and spatial presence—all of which contribute to the deeper encoding and recall of heritage narratives. Through a theoretical lens supported by the empirical literature, we argue that the interactive and embodied nature of AR/VR aligns with principles of cognitive load theory, dual coding theory, and affective neuroscience, supporting enhanced learning and memory consolidation. This paper aims to bridge the gap between technological innovation and cognitive understanding in cultural heritage dissemination, identifying concrete design principles for memory-driven digital heritage experiences. While promising, these approaches also raise important ethical considerations, including accessibility, cultural representation, and inclusivity—factors essential for equitable digital heritage dissemination. Full article

Urban green spaces (UGSs) and forests play a vital role in shaping sustainable and livable cities, offering not only ecological benefits but also spaces that are essential for human well-being, social interactions, and everyday life. Understanding the landscape features that resonate most with public preferences is essential for enhancing the appeal, accessibility, and functionality of these environments. However, traditional approaches—such as surveys or single-data analyses—often lack the nuance needed to capture the complex and multisensory nature of human responses to green spaces. This study explores a cross-modal methodology that integrates natural language processing (NLP) and deep learning techniques to analyze text and image data collected from public reviews of 19 urban parks in Nanjing. By capturing both subjective emotional expressions and objective visual impressions, this study reveals a consistent public preference for natural landscapes, particularly those featuring evergreen trees, shrubs, and floral elements. Text-based data reflect users’ lived experiences and nuanced perceptions, while image data offers insights into visual appeal and spatial composition. By bridging human-centered insights with data-driven analysis, this research provides a robust framework for evaluating landscape preferences. It also underscores the importance of designing green spaces that are not only ecologically sound but also emotionally resonant and socially inclusive. The findings offer valuable guidance for the planning, design, and adaptive management of urban green infrastructure in ways that support healthier, more responsive, and smarter urban environments. Full article