Search Results (2,670)

Planning a multi-joint minimum-effort coordinated human movement to achieve a visual goal is computationally difficult: (i) The number of anatomical elemental movements of the human body greatly exceeds the number of degrees of freedom specified by visual goals; and (ii) the mass–inertia mechanical load about each elemental movement varies not only with the posture of the body but also with the mechanical interactions between the body and the environment. Given these complications, the amount of nonlinear dynamical computation needed to plan visually-guided movement is far too large for it to be carried out within the reaction time needed to initiate an appropriate response. Consequently, we propose that, as part of motor and visual development, starting with bootstrapping by fetal and neonatal pattern-generator movements and continuing adaptively from infancy to adulthood, most of the computation is carried out in advance and stored in a motor association memory network. From there it can be quickly retrieved by a selection process that provides the appropriate movement synergy compatible with the particular visual goal. We use theorems of Riemannian geometry to describe the large amount of nonlinear dynamical data that have to be pre-computed and stored for retrieval. Based on that geometry, we argue that the logical mathematical sequence for the acquisition of these data parallels the natural development of visually- guided human movement. Full article

Human action recognition (HAR) is essential for understanding and classifying human movements. It is widely used in real-life applications such as human–computer interaction and assistive robotics. However, recognizing patterns across different temporal scales remains challenging. Traditional methods struggle with complex timing patterns, intra-class variability, and inter-class similarities, leading to misclassifications. In this paper, we propose a deep learning framework for efficient and robust HAR. It integrates residual networks (ResNet-18) for spatial feature extraction and Bi-LSTM for temporal feature extraction. A multi-head attention mechanism enhances the prioritization of crucial motion details. Additionally, we introduce a motion-based frame selection strategy utilizing optical flow to reduce redundancy and enhance efficiency. This ensures accurate, real-time recognition of both simple and complex actions. We evaluate the framework on the UCF-101 dataset, achieving a 96.60% accuracy, demonstrating competitive performance against state-of-the-art approaches. Moreover, the framework operates at 222 frames per second (FPS), achieving an optimal balance between recognition performance and computational efficiency. The proposed framework was also deployed and tested on a mobile service robot, TIAGo, validating its real-time applicability in real-world scenarios. It effectively models human actions while minimizing frame dependency, making it well-suited for real-time applications. Full article

Alzheimer’s disease, a complex neurodegenerative disease, is characterized by the pathological aggregation of insoluble amyloid β and hyperphosphorylated tau. Multiple models of this disease have been employed to investigate the etiology, pathogenesis, and multifactorial aspects of Alzheimer’s disease and facilitate therapeutic development. Mammals, especially mice, are the most common models for studying the pathogenesis of this disease in vivo. To date, the scientific literature has documented more than 280 mouse models exhibiting diverse aspects of Alzheimer’s disease pathogenesis. Other mammalian species, including rats, pigs, and primates, have also been utilized as models. Selected aspects of Alzheimer’s disease have also been modeled in simpler model organisms, such as Drosophila melanogaster, Caenorhabditis elegans, and Danio rerio. It is possible to model Alzheimer’s disease not only by creating genetically modified animal lines but also by inducing symptoms of this neurodegenerative disease. This review discusses the main methods of creating induced models, with a particular focus on modeling Alzheimer’s disease on cell cultures. Induced pluripotent stem cell (iPSC) technology has facilitated novel investigations into the mechanistic underpinnings of diverse diseases, including Alzheimer’s. Progress in culturing brain tissue allows for more personalized studies on how drugs affect the brain. Recent years have witnessed substantial advancements in intricate cellular system development, including spheroids, three-dimensional scaffolds, and microfluidic cultures. Microfluidic technologies have emerged as cutting-edge tools for studying intercellular interactions, the tissue microenvironment, and the role of the blood–brain barrier (BBB). Modern biology is experiencing a significant paradigm shift towards utilizing big data and omics technologies. Computational modeling represents a powerful methodology for researching a wide array of human diseases, including Alzheimer’s. Bioinformatic methodologies facilitate the analysis of extensive datasets generated via high-throughput experimentation. It is imperative to underscore the significance of integrating diverse modeling techniques in elucidating pathogenic mechanisms in their entirety. Full article

As the linguistic capabilities of dialogue systems improve, the importance of how they interact with humans and build trustworthy relationships is increasing. This study investigated the effect of interaction structures in a generative AI-driven dialogue system to improve relationships through interactions. The dialogue system communicated with subjects in natural language via voice and included a facial expression function. The settings of dyadic and triadic interaction structures were applied to the system. The one-to-one dyadic interaction and triadic interaction with joint attention to a topic were designed following the developmental stages of children’s social communication ability. Subjective evaluations of the dialogues and the system were conducted through a questionnaire. As a result, positive evaluations were based on well-constructed structures. The system’s inappropriate behavior under failed structures reduced the quality of the dialogues and worsened the evaluation of the system. The interaction structures in the system settings needed to match the structures intended by the subjects, whether the structures were dyadic or triadic. Under the matching and successful construction, the system fully demonstrated its dialogue capability and behaved pleasantly with the subjects. By switching interaction structures to adapt to users’ demands, system behavior becomes more appropriate for users. Full article

As urbanization accelerates, high-rise residential areas (HRRAs) have become a dominant urban housing typology. However, their complex building layouts significantly alter local wind environments, potentially impacting residents’ health. While existing studies mainly focus on macro-scale wind analysis, there is limited exploration of the micro-environmental interactions between wind conditions and human activities. This study proposes the concept of Wind Health-Vulnerable Space (WHVS) and addresses the following scientific question: How do building layouts affect local wind fields and influence pollutant accumulation and health risks, particularly for air pollutants like PM2.5 (particulate matter with an aerodynamic diameter of 2.5 μm or less), which is closely associated with adverse respiratory and cardiovascular health outcomes? To investigate this, a multidimensional framework integrating computational fluid dynamics (CFD) simulations with point-of-interest (POI) data was developed to identify and diagnose these spaces. Case studies of two typical HRRAs in Xi’an, China, reveal two types of WHVSs: (1) localized calm zones between buildings (wind speed < 0.5 m/s, pressure −0.5 to 3 Pa), where PM2.5 concentrations are 25–30% higher than surrounding areas; and (2) large-scale weak wind areas in enclosed layouts (wind speed < 0.5 m/s, pressure −1 to −2 Pa), with PM2.5 concentrations increased by 28–35%. The results highlight a dual mechanism in the formation of vulnerable spaces: wind field disturbances caused by building layout and the overlay effect of human activity distribution. This framework offers new insights and scientific support for health-oriented urban planning and building layout optimization. Full article

The maritime transportation sector is undergoing a profound shift with the emergence of autonomous vessels powered by real-time artificial intelligence (AI) agents. This article investigates the pivotal role of these agents in enhancing the safety, efficiency, and sustainability of autonomous maritime systems. Following a structured literature review, we examine the architecture of real-time AI agents, including sensor integration, communication systems, and computational infrastructure. We distinguish maritime AI agents from conventional systems by emphasizing their specialized functions, real-time processing demands, and resilience in dynamic environments. Key safety mechanisms—such as collision avoidance, anomaly detection, emergency coordination, and fail-safe operations—are analyzed to demonstrate how AI agents contribute to operational reliability. The study also explores regulatory compliance, focusing on emission control, real-time monitoring, and data governance. Implementation challenges, including limited onboard computational power, legal and ethical constraints, and interoperability issues, are addressed with practical solutions such as edge AI and modular architectures. Finally, the article outlines future research directions involving smart port integration, scalable AI models, and emerging technologies like federated and explainable AI. This work highlights the transformative potential of AI agents in advancing autonomous maritime transportation. Full article

Eye movement detection technology holds significant potential across medicine, psychology, and human–computer interaction. However, traditional methods, which primarily rely on tracking the pupil and cornea during the open-eye state, are ineffective when the eye is closed. To address this limitation, we developed a novel system capable of real-time eye movement detection even in the closed-eye state. Utilizing a micro-camera based on the OV9734 image sensor, our system captures image data to construct a dataset of eyelid images during ocular movements. We performed extensive experiments with multiple versions of the YOLO algorithm, including v5s, v8s, v9s, and v10s, in addition to testing different sizes of the YOLO v11 model (n < s < m < l < x), to achieve optimal performance. Ultimately, we selected YOLO11m as the optimal model based on its highest AP0.5 score of 0.838. Our tracker achieved a mean distance error of 0.77 mm, with 90% of predicted eye position distances having an error of less than 1.67 mm, enabling real-time tracking at 30 frames per second. This study introduces an innovative method for the real-time detection of eye movements during eye closure, enhancing and diversifying the applications of eye-tracking technology. Full article

Augmented reality (AR) is revolutionising education by integrating virtual elements into physical environments, enhancing interactivity and participation in learning processes. This study analyses the impact of AR in higher education, examining its influence on ease of adoption, student interaction, academic motivation and educational sustainability. A quantitative and explanatory design was employed, applying structural equation modelling (SmartPLS) to a sample of 4900 students from public and private universities. The results indicate that AR significantly improves the ease of adoption (β = 0.867), favouring its implementation. In addition, student interaction increases academic motivation (β = 0.597), impacting on perceived academic performance (β = 0.722) and educational sustainability (β = 0.729). These findings highlight the need to design effective learning experiences with AR to maximise their impact. However, challenges such as technological infrastructure, teacher training and equitable access must be addressed to ensure sustainable adoption. This study provides empirical evidence on the potential of AR to enhance motivation, learning and educational transformation. Future research should explore its effectiveness in diverse contexts to optimise pedagogical strategies and institutional policies. Full article

With the vast quantity of GPS data that have been collected from location-based social networks, Point-of-Interest (POI) recommendation aims to predict users’ next locations by learning from their historical check-in trajectories. While Graph Neural Network (GNN)-based models have shown promising results in this field, they typically construct single-layer graphs that fail to capture the hierarchical nature of human mobility patterns. To address this limitation, we propose a novel Hierarchical Graph Learning (HGL) framework that models POI relationships at multiple scales. Specifically, we construct a three-level graph structure: a base-level graph capturing direct POI transitions, a region-level graph modeling area-based interactions through spatio-temporal clustering, and a global-level graph representing category-based patterns. To effectively utilize this hierarchical structure, we design a cross-layer information propagation mechanism that enables bidirectional message passing between different levels, allowing the model to capture both fine-grained POI interactions and coarse-grained mobility patterns. Compared to traditional models, our hierarchical structure improves cold-start robustness and achieves superior performance on real-world datasets. While the incorporation of multi-layer attention and clustering introduces moderate computational overhead, the cost remains acceptable for offline recommendation contexts. Full article

Emotion recognition using an odor-induced electroencephalogram (EEG) has broad applications in human-computer interaction. However, existing studies often rely on subjective self-reporting to label emotion, lacking objective verification. While the β/α ratio has been identified as a potential objective indicator of arousal in EEG spectral analysis, its value in emotion recognition remains underexplored. This study ensured the authenticity of emotions through self-reporting and EEG spectral analysis of 50 adults after inhaling sandalwood essential oil (SEO) or bergamot essential oil (BEO). Classification models were built using discriminant analysis (DA), support vector machine (SVM), and random forest (RF) algorithms to identify low or high arousal emotions. Notably, this study introduced the β/α ratio as a novel frequency domain feature to enhance model performance for the first time. Both self-reporting and EEG spectral analysis indicated that SEO promotes relaxation, whereas BEO enhances attentiveness. In model testing, incorporating the β/α ratio enhanced the performance of all models, with the accuracy of DA, SVM, and RF increasing from 70%, 75%, and 85% to 75%, 80%, and 95%, respectively. This study validated the authenticity of emotions by employing a combination of subjective and objective methods and highlighted the importance of β/α in emotion recognition along the arousal dimension. Full article

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost effectiveness, and ease of deployment. The rapid advancement of the Internet of Things (IoT), Artificial Intelligence (AI), and sixth-generation mobile communication technology (6G) and Mobile Edge Computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices, and changed the image sensing and understanding to novel modes (such as machine-to-machine or human-to-machine interactions). However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. To address these issues, this paper proposes a Scalable Semantic Adaptive Communication (SSAC) for task requirement. Firstly, we design an Attention Mechanism-based Joint Source Channel Coding (AMJSCC) in order to fully exploit the correlation among semantic features, channel conditions, and tasks. Then, a Prediction Scalable Semantic Generator (PSSG) is constructed to implement scalable semantics, allowing for flexible adjustments to achieve channel adaptation. The experimental results show that the proposed SSAC is more robust than traditional and other semantic communication algorithms in image classification tasks, and achieves scalable compression rates without sacrificing classification performance, while improving the bandwidth utilization of the communication system. Full article

Flexible capacitive pressure sensors (CPSs) have been widely studied and applied due to their various advantages. Numerous studies have been carried out on improving their electromechanical sensing properties through microporous structures. However, it is challenging to effectively control these structures. In this work, we controllably fabricate a hierarchical microporous capacitive pressure sensor (HMCPS) using melt near-field electro-writing technology. Thanks to the hierarchical microporous sensor, which provides a multi-level elastic modulus and relative dielectric constants, the HMCPS shows outstanding sensing properties. Its multi-range pressure response is sensitive: 3.127 kPa⁻¹ at low pressure, 0.124 kPa⁻¹ at medium pressure, and 0.025 kPa⁻¹ at high pressure. Also, it has a stability of over 5000 cycles and a response time of less than 100 ms. The HMCPS can monitor dynamic and static pressures across a broad pressure range. It has been successfully applied to monitor human motions, showing great potential in human–computer interaction and smart wearable devices. Full article

The aim of this paper is to develop a compact, rapid-response pressure sensor for underwater propulsion. Flexible pressure sensors are widely utilized in human–computer interactions and wearable electronic devices; however, manufacturing capacitive sensors that offer a broad pressure range and high sensitivity presents significant challenges. Inspired by the dermal papillary microstructure, a capacitive pressure sensor was prepared by infusing polydimethylsiloxane (PDMS) inside an anodic aluminum oxide (AAO) template and then demolding it. The resulting pressure sensor exhibits several key characteristics: high linearity in the range of −5.2 to 6.3 kPa, a comprehensive range for both positive and negative pressure sensing in air or water environments, a quick response time of 52 ms, a recovery time of 40 ms, and excellent stability. The sensor presented in this work is innovatively applied to detect underwater negative pressure, and it is employed for the swift detection of positive and negative pressure changes in underwater thrusters. This work highlights the promising potential of biomimetic flexible capacitive pressure sensors across various applications. Full article

Trust in technology is a topic of growing importance in Human–Computer Interaction due to the growing impact of systems on daily lives. However, limited attention has been paid to how one’s national culture shapes their propensity to trust. This study addresses an existing gap in trust in technology research by advancing towards a more accurate tool for quantitatively measuring propensity to trust across different contexts. We specifically evaluate the psychometric properties of the human–computer trust scale (HCTS) in Brazil, Singapore, Malaysia, Estonia, and Mongolia. To accomplish this, we used the Measurement Invariance of Composite Models (MICOM), a procedure that examines the equivalency of the instrument’s psychometric properties across different groups. Our results highlight the importance of rigorous validation processes when applying psychometric instruments in cross-cultural contexts, offering insights into the differences between the countries investigated and the procedure’s potential to investigate trust across different groups. Full article

Intrinsic disorder refers to protein regions that lack a fixed three−dimensional structure under physiological conditions, enabling conformational plasticity. This flexibility allows for diverse functions, including transient interactions, signaling, and phase separation via disorder-to-order transitions upon binding. Our study focused on investigating the role of intrinsic disorder and liquid−liquid phase separation (LLPS) in the human acrosome, a sperm-specific organelle essential for fertilization. Using computational prediction models, network analysis, Structural Classification of Proteins (SCOP) functional assessments, and Gene Ontology, we analyzed 250 proteins within the acrosomal proteome. Our bioinformatic analysis yielded 97 proteins with high levels (>30%) of structural disorder. Further analysis of functional enrichment identified associations between disordered regions overlapping with SCOP domains and critical acrosomal processes, including vesicle trafficking, membrane fusion, and enzymatic activation. Examples of disordered SCOP domains include the PLC-like phosphodiesterase domain, the t-SNARE domain, and the P-domain of calnexin/calreticulin. Protein–protein interaction networks revealed acrosomal proteins as hubs in tightly interconnected systems, emphasizing their functional importance. LLPS propensity modeling determined that over 30% of these proteins are high-probability LLPS drivers (>60%), underscoring their role in dynamic compartmentalization. Proteins such as myristoylated alanine-rich C-kinase substrate and nuclear transition protein 2 exhibited both high LLPS propensities and high levels of structural disorder. A significant relationship (p < 0.0001, R² = 0.649) was observed between the level of intrinsic disorder and LLPS propensity, showing the role of disorder in facilitating phase separation. Overall, these findings provide insights into how intrinsic disorder and LLPS contribute to the structural adaptability and functional precision required for fertilization, with implications for understanding disorders associated with the human acrosome reaction. Full article