Search Results (61)

Background: Sensorimotor differences have frequently been reported in children with developmental dyslexia, but are often considered secondary or comorbid to phonological deficits. Within an embodied cognition perspective, reading acquisition emerges from dynamic interactions between bodily regulation, multisensory integration, and learning-related neural plasticity. Proprioception contributes to spatial orientation, motor coordination, and perceptual stabilization, while sleep-dependent processes play a critical role in the consolidation and automatization of cognitive and motor skills. Objectives: Building on early clinical observations, including the hypothesis proposed by Martins da Cunha, this review explores whether variations in proprioceptive processing and sensorimotor regulation may influence multisensory stability and the conditions under which reading skills develop in some individuals with dyslexia. Methods: This narrative synthesis integrates clinical observations and experimental paradigms examining proprioceptive function in children with dyslexia, including studies conducted in our laboratory over the past two decades. These investigations address postural regulation under varying attentional demands, laboratory measures of proprioceptive acuity, visuospatial localization tasks, multisensory interactions, and exploratory observations concerning sleep–wake regulation. Results: Across studies, children with dyslexia often show differences in proprioceptive processing associated with variations in postural regulation, visuospatial stability, and multisensory tasks. Laboratory measurements suggest reduced proprioceptive acuity in some individuals, with moderate correlations observed between proprioceptive sensitivity and reading-related measures. Additional observations suggest that nocturnal physiological regulation—including respiratory dynamics and sleep architecture—may interact with daytime sensorimotor stability and attentional functioning. Conclusions: Taken together, these findings support the hypothesis that variations in sensorimotor regulation across the sleep–wake cycle may influence the stability of multisensory processing and attentional conditions relevant for reading acquisition. Within this perspective, proprioception is not proposed as an alternative explanation for dyslexia but as a complementary dimension that may contribute to the heterogeneity of dyslexic profiles. Further longitudinal and controlled studies are required to clarify the relationships between sensorimotor regulation, sleep-dependent plasticity, and learning processes. Full article

Alzheimer’s disease (AD) is the most common cause of dementia, affecting 55 million people worldwide. Its characteristics include the accumulation of senile plaques and neurofibrillary tangles. This disease is associated with changes in the concentration of AD biomarkers, such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains. Due to the fact that neuropathological processes begin decades before the onset of cognitive symptoms, accurate detection of AD biomarkers is crucial for its early diagnosis. The combination of analytical techniques and machine learning methods plays a crucial role in medical innovation. Recently, efforts have been made to develop machine learning-assisted biosensors for AD diagnosis. This article provides an overview of the progress in machine learning-assisted sensing of AD biomarkers in bodily fluids. It mainly includes three parts: machine learning algorithms, machine learning-assisted electrochemical and optical biosensors, and challenges and future perspectives. We believe that this work will contribute to the development of innovative analytical devices based on artificial intelligence for monitoring and managing neurodegenerative diseases. Full article

Sex education for students with autism spectrum disorder in Saudi Arabia remains limited and underdeveloped, raising concerns related to safety, body awareness, and healthy personal development during the school years. This qualitative study employed semi-structured interviews with four teachers and three family members and was analyzed using thematic analysis. Participants discussed school-aged autistic children educated in mainstream inclusive settings alongside peers with diverse learning profiles. All students referenced were verbally communicative, and some were reported to have co-occurring developmental or behavioral conditions. The findings revealed key challenges, including heightened vulnerability to harassment, limited understanding of bodily boundaries, and difficulties related to personal hygiene and privacy. Participants also identified substantial gaps in existing curricula, inconsistent teacher preparation, and limited access to guidance for families, resulting in fragmented approaches to sex education. The findings highlight the urgent need for culturally responsive, developmentally appropriate sex education curricula, targeted professional development for teachers and families, and strengthened collaboration between home and school. Such efforts are essential to promote safety, well-being, and protection for autistic students within the Saudi educational context. Full article

The World Health Organization identifies creative thinking as a key life skill essential for health promotion, personal development, and well-being. In line with recent perspectives on motor learning within the ecological dynamics approach, this study highlights the importance of self-organization, free initiative, and divergent thinking as processes that are deeply connected to individual emotional, experiential, and bodily engagement within dynamic environments. With this quasi-experimental study, conducted in Italy, we aimed to examine the impact of a physical education program, designed according to the principles of ecological dynamics, on the development of creative thinking in children. The sample included 107 primary school students (58 girls, 49 boys; mean age = 7.51 ± 0.50 years) who were randomly assigned to either an experimental group (n = 57) or a control group (n = 50). Creative thinking was assessed before and after the intervention using the WCR test. The WCR (Widening, Connecting, and Reorganizing) test assesses three core components of creative thinking through age-appropriate visual and verbal tasks. The results showed that there was a significant improvement (p < 0.05) in cognitive widening for the experimental group compared with the control group. The findings of this study suggest that physical education grounded in the ecological dynamics framework promotes the generation of ideas, cognitive flexibility, and motor adaptability, allowing children to explore original and self-determined movement solutions. Such programs may play a crucial role in supporting creativity and holistic development in educational contexts. Full article

Generative artificial intelligence (AI) is reshaping creative practices, yet many systems rely on traditional interfaces, limiting intuitive and embodied engagement. This study presents a qualitative observational analysis of participant interactions with a real-time generative AI installation designed to co-create Ukiyo-e-style artwork through embodied inputs. The system dynamically interprets physical presence, object manipulation, body poses, and gestures to influence AI-generated visuals displayed on a large public screen. Drawing on systematic video analysis and detailed interaction logs across 13 sessions, the research identifies core modalities of interaction, patterns of co-creation, and user responses. Tangible objects with salient visual features such as color and pattern emerged as the primary, most intuitive input method, while bodily poses and hand gestures served as compositional modifiers. The system’s immediate feedback loop enabled rapid learning and iterative exploration and enhanced the user’s feeling of control. Users engaged in collaborative discovery, turn-taking, and shared authorship, frequently expressing a positive effect. The findings highlight how embodied interaction lowers cognitive barriers, enhances engagement, and supports meaningful human–AI collaboration. This study offers design implications for future creative AI systems, emphasizing accessibility, playful exploration, and cultural resonance, with the potential to democratize artistic expression and foster deeper public engagement with digital cultural heritage. Full article

While existing research highlights a positive association between computational thinking (CT) and self-regulation (SR) skills, limited attention has been given to the embodied and social processes within CT activities that support young children’s executive functions (EFs)—key components of SR. This study investigates how preschoolers develop basic and higher-order EFs, such as focused attention, inhibitory control, causal reasoning, and problem-solving, through their engagement with a tangible programming toy in teacher-guided small groups in a university-affiliated preschool. Informed by a we-syntonicity framework that integrates Papert’s concepts of body/ego syntonicity and Schutz’s “we-relationship”, we conducted a multimodal microanalysis of video-recorded group sessions. Our analysis focuses on two sessions, the “Obstacle Challenge” and “Conditionals”, featuring four excerpts. Findings reveal that children leverage bodily knowledge and empathy toward the toy—named Rapunzel—to sustain attention, manage impulses, reason about cause-effect, and collaborate on problem-solving. Three agents shape these processes: the toy, fostering collective engagement; the teacher, scaffolding learning and emotional regulation; and the children, coordinating actions and sharing affective responses. These findings challenge traditional views of SR as an individual cognitive activity, framing it instead as an embodied, social, and situated practice. This study underscores the importance of collaborative CT activities in fostering SR during early childhood. Full article

Despite increased interest in embodied learning, the role of sensorimotor activity in shaping students’ probabilistic reasoning remains underexplored. This design-based study examines how high school students develop key probabilistic concepts, including sample space, certainty, and event probability, through whole-body movement activities situated in an authentic classroom setting. Grounded in embodied cognition theory, we introduce a two-axis interpretive framework. One axis spans sensorimotor exploration and formal reasoning, drawing from established continuums in the literature. The second axis, derived inductively from our analysis, contrasts engagement with distraction, foregrounding the affective and attentional dimensions of embodied participation. Students engaged in structured yet open-ended movement sequences that elicited intuitive insights. This approach, epitomized by one student’s spontaneous question, “Can I use my leg too?”, captures the agentive and improvisational character of the embodied learning environment. Through five analyzed classroom episodes, we trace how students shifted between bodily exploration and formalization, often through nonlinear trajectories shaped by play, uncertainty, and emotionally driven reflection. While moments of insight emerged organically, they were also fragile, as they were affected by ambiguity and the difficulty in translating physical actions into mathematical language. Our findings underscore the pedagogical potential of embodied design for probabilistic learning while also highlighting the need for responsive teaching that balances structure with improvisation and supports affective integration throughout the learning process. Full article

Our paper introduces a novel idea of a virtual mouse character driven by gesture detection, eye-tracking, and voice monitoring. This system uses cutting-edge computer vision and machine learning technology to let users command and control the mouse pointer using eye motions, voice commands, or hand gestures. This system’s main goal is to provide users who want a more natural, hands-free approach to interacting with their computers as well as those with impairments that limit their bodily motions, such as those with paralysis—with an easy and engaging interface. The system improves accessibility and usability by combining many input modalities, therefore providing a flexible answer for numerous users. While the speech recognition function permits hands-free operation via voice instructions, the eye-tracking component detects and responds to the user’s gaze, therefore providing exact cursor control. Gesture recognition enhances these features even further by letting users use their hands simply to execute mouse operations. This technology not only enhances personal user experience for people with impairments but also marks a major development in human–computer interaction. It shows how computer vision and machine learning may be used to provide more inclusive and flexible user interfaces, therefore improving the accessibility and efficiency of computer usage for everyone. Full article

This article reports on a Swedish study investigating how children aged 2–3 years experience mathematical concepts through representations in play-responsive teaching. Drawing on the semiotic–cultural theory of learning, this study examines how representations, such as spoken language, bodily action, and artifacts, are mediated. Video-recorded teaching sessions are analyzed to identify semiotic means of objectification and semiotic nodes at which these representations converge. The analysis distinguishes between children encountering concepts expressed by others and expressing concepts themselves. The results indicate that play-responsive teaching creates varied opportunities for experiencing mathematical concepts, with distinct modes of sensuous cognition linked to whether a concept is encountered or expressed. This study underscores the role of teachers’ choices in shaping these experiences and highlights bodily action as a significant form of representation. These findings aim to inform the use of representations in teaching mathematics to the youngest children in preschool. Full article

Insurance is essential for financial risk protection, but claim management is complex and requires accurate classification and forecasting strategies. This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. The research analyses the variables of claims, vehicles, and insured parties that influence the classification of high-cost claims. This investigation utilizes a dataset comprising 802 observations of bodily injury claims from the motor liability portfolio of a private insurance company in Albania, covering the period from 2018 to 2024. In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. At the same time, logistic regression demonstrated the weakest performance. Key predictive factors in high claim classification include claim type, deferred period, vehicle brand and age of driver. These findings highlight the potential of machine learning models in improving claim classification and risk assessment and refine underwriting policy. Full article

This study investigates the effects of embodied (breathing exercises) and cognitive interventions on adolescents’ flow experience and cognition patterns. Using a mixed-methods design, 303 vocational high school students were assigned to three groups: Embodied Task Group (N = 108), Cognitive Task Group (N = 100), and Mental Health Course Group (N = 95). Experiment 1 employed a 3×2 Multivariate Analysis of Covariance (MANCOVA) design to compare flow experience dimensions, while Experiment 2 used Epistemic Network Analysis (ENA) to analyze diary entries. Results showed that the Embodied Task Group outperformed the Cognitive Task Group in “Unambiguous Feedback” (η_p² = 0.01, a small effect) and had higher “Transformation of Time” (η_p² = 0.01, a small effect) than the Mental Health Course Group. ENA revealed that the Embodied Group developed stronger body-environment interaction patterns, shifting cognition pattern from psychological evaluations to dynamic bodily processes over time. Conversely, the Cognitive Task Group maintained event-focused cognition with weaker mind–body integration. Findings highlight breathing exercises’ potential to enhance flow experience through embodied awareness and multisensory processing, offering practical implications for mental health education by promoting embodied learning tasks to foster flow experience. Full article

Motor imagery (MI) is a crucial research field within the brain–computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite the significant clinical and application value of MI-BCI technology, accurately decoding high-dimensional and low signal-to-noise ratio (SNR) electroencephalography (EEG) signals remains challenging. Moreover, traditional deep learning approaches exhibit limitations in processing EEG signals, particularly in capturing the intrinsic correlations between electrode channels and long-distance temporal dependencies. To address these challenges, this research introduces a novel end-to-end decoding network that integrates convolutional neural networks (CNNs) and a Swin Transformer, aiming at enhancing the classification accuracy of the MI paradigm in EEG signals. This approach transforms EEG signals into a three-dimensional data structure, utilizing one-dimensional convolutions along the temporal dimension and two-dimensional convolutions across the EEG electrode distribution for initial spatio-temporal feature extraction, followed by deep feature exploration using a 3D Swin Transformer module. Experimental results show that on the BCI Competition IV-2a dataset, the proposed method achieves 83.99% classification accuracy, which is significantly better than the existing deep learning methods. This finding underscores the efficacy of combining a CNN and Swin Transformer in a 3D data space for processing high-dimensional, low-SNR EEG signals, offering a new perspective for the future development of MI-BCI. Future research could further explore the applicability of this method across various BCI tasks and its potential clinical implementations. Full article

This study examines the effects of implementing a movement-integrated instruction (MII) program in third-grade mathematics classes with a focus on students’ mathematical learning outcomes and physical literacy development. The program was designed using the Analysis, Design, Development, Implementation and Evaluation (ADDIE) instructional model and was implemented in a public elementary school in South Korea. While the primary instructional emphasis was placed on improving mathematical concept comprehension and problem solving, the study also evaluated outcomes in three core areas of physical literacy: physical competence, motivation and confidence, and knowledge and understanding of physical activity. A descriptive qualitative approach was adopted and supplemented with quantitative data. The data sources included classroom observations, learning artifacts, teacher reflections, semi-structured interviews, and structured student surveys. The results showed that 82.6% of students reported improved bodily control and coordination, while 75.4% indicated that they used skills acquired through physical education (PE) to solve math problems. Student work demonstrated an increasing use of multi-step reasoning, diagrammatic representations, and contextual explanations, suggesting that embodied learning reinforces both cognitive engagement and physical development. Although challenges related to time, space, and varying motor abilities were encountered, they were addressed through interdisciplinary integration and differentiated instructional strategies. This study provides empirical support for MII as a pedagogical model that effectively bridges academic learning and physical development, and offers practical recommendations for broader applications in elementary education. Full article

Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users. Now, ballistocardiogram (BCG) and impedance plethysmogram (IPG) could be used to perform the cuffless BP measurement. Thus, the aim of this study is to realize edge computing for the BP measurement in real time, which includes measurements of BCG and IPG signals, digital signal process, feature extraction, and BP estimation by machine learning algorithm. This system measured BCG and IPG signals from a bodily weight-fat scale with the self-made circuits. The signals were filtered to reduce the noise and segmented by 2 s. Then, we proposed a flowchart to extract the parameter, pulse transit time (PTT), within each segment. The feature included two calibration-based parameters and one calibration-free parameter was used to estimate BP with XGBoost. In order to realize the system in STM32F756ZG NUCLEO development board, we limited the hyperparameters of XGBoost model, including maximum depth (max_depth) and tree number (n_estimators). Results show that the error of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in server-based computing are 2.64 ± 9.71 mmHg and 1.52 ± 6.32 mmHg, and in edge computing are 2.2 ± 10.9 mmHg and 1.87 ± 6.79 mmHg. This proposed method significantly enhances the feasibility of bodily weight-fat scale in the BP measurement for effective utilization in mobile health applications. Full article

Background: Children with learning disability (LD) often experience a poor quality of life (QOL) compared to their peers without a known history of LD. Coping strategies are known to play a role in influencing their QOL. Objectives: This study aims to compare the QOL and coping strategies between children with and without LD. Additionally, it seeks to evaluate how coping strategies impact the QOL of children with LD in the Eastern Governorate of Saudi Arabia. Method: A representative sample of 6 to 18-year-old children with (n = 97) and without (n = 89) LD were recruited from different schools. The Short Form-12 (SF-12) health survey was used to assess both physical and mental health components, while the validated Coping Orientation to Problems Experienced Inventory (Brief-COPE) measured coping strategies. Data analysis included descriptive statistics (mean, standard deviation, percentage), independent t-tests, Spearman’s correlation, and binary logistic regression. Results: The results reveal that participants with LD show poor QOL in terms of role functioning, bodily pain, general health, vitality, social functioning, role emotion, and mental health in comparison to non-disabled children. Participants with LD show greater reliance on substance abuse and religious coping than non-disabled children. The results clearly indicate a fairly to moderately strong correlation between the physical component summary and all approaches to coping strategies except religious coping. Of all the approaches to coping methods, we observe a weak correlation among denial (r = −0.17, p < 0.05), substance abuse (r = −0.15, p < 0.05), and behavioral disengagement (r = −0.18, p < 0.05) with the mental component summary aspect of QOL. The results of logistic regression analysis indicate that grade (OR = 3.79; p = 0.01) is significantly related to LD. The physical component summary score is significantly associated with denial (β = −0.33, CI = −6.87–−2.19, p < 0.01), and substance abuse (β = −0.14, CI = −4.96–0.40, p < 0.05), while the mental component summary is significantly associated with active coping (β = −0.30, CI = −4.50–0.76, p < 0.01), behavioral disengagement (β = −0.20, CI = −4.48–0.30, p < 0.05), and humor coping strategy (β = 0.22, CI = 0.06–4.55, p < 0.05). Conclusion: These findings are relevant to researchers, psychologists, special educators, teachers, and clinicians, given the need to understand the coping variables to improve the QOL of these learning-disabled children. Full article