Search Results (836)

A driver in road hypnosis has two different types of characteristics. One is the external characteristics, which are distinct and can be directly observed. The other is internal characteristics, which are indistinctive and cannot be directly observed. The eye movement characteristic, as a distinct external characteristic, is one of the typical characteristics of road hypnosis identification. The electroencephalogram (EEG) characteristic, as an internal feature, is a golden parameter of drivers’ life identification. This paper proposes an identification method for road hypnosis based on the fusion of human life parameters. Eye movement data and EEG data are collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with principal component analysis (PCA) and independent component analysis (ICA), respectively. Eye movement data can be trained with a self-attention model (SAM), and the EEG data can be trained with the deep belief network (DBN). The road hypnosis identification model can be constructed by combining the two trained models with the stacking method. Repeated Random Subsampling Cross-Validation (RRSCV) is used to validate models. The results show that road hypnosis can be effectively recognized using the constructed model. This study is of great significance to reveal the essential characteristics and mechanisms of road hypnosis. The effectiveness and accuracy of road hypnosis identification can also be improved through this study. Full article

Autoimmune glomerulonephritis is a homogeneous area of renal pathology with clinical relevance in terms of its numerical impact and difficulties in its treatment. Systemic lupus erythematosus/lupus nephritis and membranous nephropathy are the two most frequent autoimmune conditions with clinical relevance. They are characterized by glomerular deposition of circulating autoantibodies that recognize glomerular antigens. Technologies for studying renal tissue and circulating antibodies have evolved over the years and have culminated with the direct analysis of antigen–antibody complexes in renal bioptic fragments. Initial studies utilized renal microdissection to obtain glomerular tissue. Obtaining immunoprecipitates after partial proteolysis of renal tissue is a recent evolution that eliminates the need for tissue microdissection. New technologies based on ‘super-resolution microscopy’ have added the possibility of a direct analysis of the interaction between circulating autoantibodies and their target antigens in glomeruli. Peptide and protein arrays represent the new frontier for identifying new autoantibodies in circulation. Peptide arrays consist of 7.5 million aligned peptides with 16 amino acids each, which cover the whole human proteome; protein arrays utilize, instead, a chip containing structured proteins, with 26.000 overall. An example of the application of the peptide array is the discovery in membranous nephropathy of many new circulating autoantibodies including formin-like-1, a protein of podosomes that is implicated in macrophage movements. Studies that utilize protein arrays are now in progress and will soon be published. The contribution of new technologies is expected to be relevant for extending our knowledge of the mechanisms involved in the pathogenesis of several autoimmune conditions. They may also add significant tools in clinical settings and modify the therapeutic handling of conditions that are not considered to be autoimmune. Full article

Background: In safety-critical environments, human error is a leading cause of accidents, with the loss of situation awareness (SA) being a key contributing factor. Accurate SA assessment is essential for minimizing such risks and ensuring operational safety. Traditional SA measurement methods have limitations in dynamic real-world settings, while physiological signals, particularly EEG, offer a non-invasive, real-time alternative for continuous SA monitoring. However, the reliability of SA measurement based on physiological signals depends on the accuracy of SA labeling. Objective: This study aims to design an effective SA measurement paradigm specific to high-speed train driving, investigate more accurate physiological signal-based SA labeling methods, and explore the relationships between SA levels and key physiological metrics based on the developed framework. Methods: This study recruited 19 male high-speed train driver trainees and developed an SA measurement paradigm specific to high-speed train driving. A method combining subjective SA ratings and task performance was introduced to generate accurate SA labels. Results: The results of statistical analysis confirmed the effectiveness of this paradigm in inducing SA level changes, revealing significant relationships between SA levels and key physiological metrics, including eye movement patterns, ECG features (e.g., heart rate variability), and EEG power spectral density across theta, alpha, and beta bands. Conclusions: This study supports the use of multimodal physiological signals for SA assessment and provides a theoretical foundation for future applications of SA monitoring in railway operations, contributing to enhanced operational safety. Full article

The measurement of human breathing is crucial for assessing the condition of the body. It opens up possibilities for various intelligent applications, like advanced medical monitoring and sleep analysis. Conventional approaches relying on wearable devices tend to be expensive and inconvenient for users. Recent research has shown that inexpensive WiFi devices commonly available in the market can be utilized effectively for non-contact breathing monitoring. WiFi-based breathing monitoring is highly sensitive to motion during the breathing process. This sensitivity arises because current methods primarily rely on extracting breathing signals from the amplitude and phase variations of WiFi Channel State Information (CSI) during breathing. However, these variations can be masked by body movements, leading to inaccurate counting of breathing cycles. To address this issue, we propose a method for extracting breathing signals based on the trajectories of two-chain CSI ratios on the I/Q plane. This method accurately monitors breathing by tracking and identifying the inflection points of the CSI ratio samples’ trajectories on the I/Q plane throughout the breathing cycle. We propose a dispersion model to label and filter out CSI ratio samples representing significant motion interference, thereby enhancing the robustness of the breathing monitoring system. Furthermore, to obtain accurate breathing waveforms, we propose a method for fitting the trajectory curve of the CSI ratio samples. Based on the fitted curve, a breathing segment extraction algorithm is introduced, enabling precise breathing monitoring. Our experimental results demonstrate that this approach achieves minimal error and significantly enhances the accuracy of WiFi-based breathing monitoring. Full article

Mandible is a bony structure of neuroectodermal origin with unique characteristics that support dentition and jaw movements. In the present study, we investigated the effects of gestational exposure to a mixture of endocrine-disrupting chemicals (EDCs) on mandibular growth in mice. The mixture under study (Mixture N1) has been associated with neurodevelopmental effects in both a human cohort and animal studies. Pregnant mice were exposed throughout gestation to 0.5× (times of pregnant women’s exposure levels), 10×, 100× and 500× of Mixture N1, or the vehicle, and the mandibles of the male offspring were studied in adulthood. Micro-CT analysis showed non-monotonic effects of Mixture N1 in the distances between specific mandibular landmarks and in the crown width of M1 molar, as well as changes in the mandibular bone characteristics. The alveolar bone volume was reduced, and the trabecular separation was increased in the 500× exposed mice. Bone volume in the condyle head was increased in all treated groups. Τhe Safranin-O-stained area of mature hypertrophic chondrocytes and the width of their zones were reduced in 0.5×, 10× and 100× exposed groups. This is the first indication that prenatal exposure to an epidemiologically defined EDC mixture, associated with neurodevelopmental impacts, can also affect mandibular growth in mammals. Full article

Decision trees are a widely used machine learning technique due to their ease of interpretation and construction. This method allows domain experts to learn from raw data, but they cannot include their prior knowledge in the analysis due to its automatic nature, which implies minimal human intervention in its computation. Conversely, interactive visualization methods have proven to be effective in gaining insights from data, as they incorporate the researcher’s criteria into the analysis process. In an effort to combine both methodologies, we have developed a tool to manually build decision trees according to subsequent visualizations of data mapping after applying linear discriminant analysis in combination with Star Coordinates in order to analyze the importance of each feature in the separation. The nodes’ information contains data about the features that can be used to split and their cut-off values, in order to select them in a guided manner. In this way, it is possible to produce simpler and more expertly driven decision trees than those obtained by automatic methods. The resulting decision trees reduces the tree size compared to those generated by automatic machine learning algorithms, obtaining a similar accuracy and therefore improving their understanding. The tool developed and presented here to manually create decision trees in a guided manner based on the subsequent visualizations of the data mapping facilitates the use of this method in real-world applications. The usefulness of this tool is demonstrated through a case study with a complex dataset used for motion recognition, where domain experts built their own decision trees by applying their prior knowledge and the visualizations provided by the tool in node construction. The resulting trees are more comprehensible and explainable, offering valuable insights into the data and confirming the relevance of upper body features and hand movements for motion recognition. Full article

The emotional expression of body movement, which is an aspect of emotional communication between humans, has not been considered enough in the field of human–robot interactions (HRIs). This paper explores human perceptions of the emotional expressions of humanoid robot body movements to study the emotional design of the bodily expressions of robots and the characteristics of the human perception of these emotional body movements. Six categories of emotional behaviors, including happiness, anger, sadness, surprise, fear, and disgust, were designed by imitating human emotional body movements, and they were implemented on a Yanshee robot. A total of 135 participants were recruited for questionnaires and eye-tracking measurements. Statistical methods, including K-means clustering, repeated analysis of variance (ANOVA), Friedman’s ANOVA, and Spearman’s correlation test, were used to analyze the data. According to the statistical results of emotional categories, intensities, and arousals perceived by humans, a guide to grading the designed robot’s bodily expressions of emotion is created. By combining this guide with certain objective analyses, such as fixation and trajectory of eye movements, the characteristics of human perception, including the perceived differences between happiness and negative emotions and the trends of eye movements for different emotional categories, are described. This study not only illustrates subjective and objective evidence that humans can perceive robot bodily expressions of emotions through only vision but also provides helpful guidance for designing appropriate emotional bodily expressions in HRIs. Full article

Background/Objectives: The incorporation of biomechanics into stroke neurorehabilitation may serve to strengthen the effectiveness of rehabilitation strategies by increasing our understanding of human movement and recovery processes. The present bibliometric analysis of biomechanics research in stroke neurorehabilitation is conducted with the objectives of identifying influential studies, key trends, and emerging research areas that would inform future research and clinical practice. Methods: A comprehensive bibliometric analysis was performed using documents retrieved from the Scopus database on 6 August 2024. The analysis included performance metrics such as publication counts and citation analysis, as well as science mapping techniques, including co-authorship, bibliographic coupling, co-citation, and keyword co-occurrence analyses. Data visualization tools such as VOSviewer and Power BI were utilized to map the bibliometric networks and trends. Results: An overabundance of recent work has yielded substantial advancements in the application of brain–computer interfaces to electroencephalography and functional neuroimaging during stroke neurorehabilitation., which translate neural activity into control signals for external devices and provide critical insights into the biomechanics of motor recovery by enabling precise tracking and feedback of movement during rehabilitation. A sampling of the most impactful contributors and influential publications identified two leading countries of contribution: the United States and China. Three prominent research topic clusters were also noted: biomechanical evaluation and movement analysis, neurorehabilitation and robotics, and motor recovery and functional rehabilitation. Conclusions: The findings underscore the growing integration of advanced technologies such as robotics, neuroimaging, and virtual reality into neurorehabilitation practices. These innovations are poised to enhance the precision and effectiveness of therapeutic interventions. Future research should focus on the long-term impacts of these technologies and the development of accessible, cost-effective tools for clinical use. The integration of multidisciplinary approaches will be crucial in optimizing patient outcomes and improving the quality of life for stroke survivors. Full article

Cities are in constant development, both structurally and demographically, necessitating careful planning to enhance their orderliness and livability. This research focuses on identifying development directions and routes for the Kyrenia White Zone, situated between the sea and the mountains in northern Cyprus, a significant tourist area. The rapid implementation of zoning laws over different periods has led to swift development and population growth, resulting in various infrastructure challenges, particularly related to transportation. The primary aim of this study is to assess the current infrastructure issues within the zone, understand user perceptions, and identify key factors influencing future growth. Based on the collected data, we propose an alternative growth area for the future development plan of the city. Additionally, this research seeks to explore irregular urban developments and make informed design decisions for their future. Utilizing Space Syntax and GIS as core methodologies, the study employs Space Syntax, a research method developed by Bill Hillier and Julienne Hanson in the 1970s, to analyze human movement and perception. The existing map system of the Kyrenia White Zone was digitized, and essential geographical information was gathered. This data were analyzed using GIS and evaluated through the Space Syntax method. The analysis yielded alternative growth routes that address current challenges within the zone, accompanied by recommendations for enhancing its future development. Full article

Motor skill development is crucial in human growth, evolving with the maturation of the nervous and musculoskeletal systems. Quantifying these skills, especially coordinative abilities, remains challenging. This study aimed to assess the performance of five motor tasks in children and adolescents using high-speed video analysis, providing data for movement and health professionals. Seventy-two volunteers were divided into three age groups: 27 first-grade primary school students (19 males and 8 females, aged 6.5 ± 0.5 years), 35 fourth-grade primary school students (16 males and 19 females, aged 9.2 ± 0.4 years), and 28 s-year middle school students (16 males and 12 females, aged 13.0 ± 0.3 years). Participants performed five motor tasks: standing long jump, running long jump, stationary ball throw, running ball throw, and sprint running. Each task was recorded at 120 frames per second and analyzed using specialized software to measure linear and angular kinematic parameters. Quantitative measurements were taken in the sagittal plane, while qualitative observations were made using a dichotomous approach. Statistical analysis was performed using the Kruskal–Wallis and Mann–Whitney tests with Bonferroni correction. Significant differences were observed across age groups in various parameters. In the standing long jump, older participants exhibited a longer time between initial movement and maximum loading. The running long jump revealed differences in the take-off angle, with fourth-grade students performing the best. Ball-throwing tests indicated improvements in the release angle with age, particularly in females. Sprint running demonstrated the expected improvements in time and stride length with age. Gender differences were notable in fourth-grade students during the running long jump, with females showing greater knee flexion, while males achieved better take-off angles. Video analysis effectively identified age-related and gender-specific differences in motor skill performance. The main differences were measured between first-grade primary school and second-year middle school students while gender differences were limited to all age groups. This method provides valuable insights into motor development trajectories and can be used by professionals to objectively assess and monitor the technical aspects of motor skills across different age groups. Full article

This paper examines the influence of emotions on political polarization, looking at online propagation of conspiracy thinking by extreme right movements in Southern Europe. Integrating insights from psychology, political science, media studies, and system theory, we propose the ‘polarization loop’, a causal mechanism explaining the cyclical relationship between extreme messages, emotional engagement, media amplification, and societal polarization. We illustrate the utility of the polarization loop observing the use of the Great Replacement Theory by extreme right movements in Italy, Portugal, and Spain. We suggest possible options to mitigate the negative effects of online polarization in democracy, including public oversight of algorithmic decission-making, involving social science and humanities in algorithmic design, and strengthening resilience of citizenship to prevent emotional overflow. We encourage interdisciplinary research where historical analysis can guide computational methods such as Natural Language Processing (NLP), using Large Language Models fine-tunned consistently with political science research. Provided the intimate nature of emotions, the focus of connected research should remain on structural patterns rather than individual behavior, making it explicit that results derived from this research cannot be applied as the base for decisions, automated or not, that may affect individuals. Full article

Humans have the unique ability to discern spatial and temporal regularities in their surroundings. However, the effect of learning these regularities on eye movement characteristics has not been studied enough. In the present study, we investigated the effect of the frequency of occurrence and the presence of common chunks in visual images on eye movement characteristics like the fixation duration, saccade amplitude and number, and gaze number across sequential experimental epochs. The participants had to discriminate the patterns presented in pairs as the same or different. The order of pairs was repeated six times. Our results show an increase in fixation duration and a decrease in saccade amplitude in the sequential epochs, suggesting a transition from ambient to focal information processing as participants acquire knowledge. This transition indicates deeper cognitive engagement and extended analysis of the stimulus information. Interestingly, contrary to our expectations, the saccade number increased, and the gaze number decreased. These unexpected results might imply a reduction in the memory load and a narrowing of attentional focus when the relevant stimulus characteristics are already determined. Full article

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients. Full article

Motion analysis is of great interest to a variety of applications, such as virtual and augmented reality and medical diagnostics. Hand movement tracking systems, in particular, are used as a human–machine interface. In most cases, these systems are based on optical or acceleration/angular speed sensors. These technologies are already well researched and used in commercial systems. In special applications, it can be advantageous to use magnetic sensors to supplement an existing system or even replace the existing sensors. The core of a motion tracking system is a localization unit. The relatively complex localization algorithms present a problem in magnetic systems, leading to a relatively large computational complexity. In this paper, a new approach for pose estimation of a kinematic chain is presented. The new algorithm is based on spatially rotating magnetic dipole sources. A spatial feature is extracted from the sensor signal, the dipole direction in which the maximum magnitude value is detected at the sensor. This is introduced as the “maximum vector”. A relationship between this feature, the location vector (pointing from the magnetic source to the sensor position) and the sensor orientation is derived and subsequently exploited. By modelling the hand as a kinematic chain, the posture of the chain can be described in two ways: the knowledge about the magnetic correlations and the structure of the kinematic chain. Both are bundled in an iterative algorithm with very low complexity. The algorithm was implemented in a real-time framework and evaluated in a simulation and first laboratory tests. In tests without movement, it could be shown that there was no significant deviation between the simulated and estimated poses. In tests with periodic movements, an error in the range of 1° was found. Of particular interest here is the required computing power. This was evaluated in terms of the required computing operations and the required computing time. Initial analyses have shown that a computing time of 3 $\mathsf{\mu }$s per joint is required on a personal computer. Lastly, the first laboratory tests basically prove the functionality of the proposed methodology. Full article

InterCriteria analysis (ICrA) and two kinds of correlation analyses, Pearson (PCA) and Spearman (SCA), were applied to surface electromyography (sEMG) signals obtained from human arm movements in the horizontal plane. Ten healthy participants performed ten movements, eight of which were cyclic. Each cyclic movement (CM) consisted of flexion and extension phases with equal duration (10 s, 6 s, 2 s, and 1 s) and two 5 s rest poses between them. The CMs were performed with and without an added load of 0.5 kg on the wrists of the participants. The sEMG signals from six different muscles or separate muscle heads (m. deltoideus pars clavicularis, m. deltoideus pars spinata, m. brachialis, m. anconeus, m. biceps brachii, and m. triceps brachii long head) were recorded and used to compare the results of the ICrA, PCA, and SCA. All three methods found identical consonance pairs for the flexion and extension CM phases. Additionally, PCA detected two more consonance pairs in the extension phases. In this investigation, ICrA, PCA, and SCA were proven to be reliable tools when applied separately or in combination for sEMG data. These three methods are appropriate for researching arm movements in the horizontal plane and experimental protocol revision. Full article