Search Results (429)

Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string (“fear detection” AND “artificial intelligence” OR “machine learning” AND NOT “fnirs OR mri OR ct OR pet OR image”). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing. Full article

Objectives: Older adults who worry about not being able to stand up from the floor after a fall, reduce their physical activity, which leads to a higher risk of falling. The Backward Chaining Method (BCM) was developed specifically for this population to safely teach and practice the movement sequence required to stand up from the floor. Our aim is to evaluate the effectiveness of using the BCM to teach older adults how to stand up from the floor, and to determine whether this training has an impact on functional mobility, muscle strength, fear of falling, and life-space mobility. Methods: A total of 26 residents of a long-term care facility were randomly allocated to two groups. Residents in the intervention group (IG, n = 13) participated in a seven-week training program to learn how to stand up from the floor with BCM, in addition to the usual care generally offered in long-term care facilities. The participants in the control group (CG, n = 13) received the usual care alone. The primary outcome measure was functional mobility, assessed by the Timed Up and Go test. Secondary outcome measures included functional lower limb strength, grip strength, fear of falling, and life-space mobility. The outcomes were measured at baseline and after the seven-week intervention period. Results: We found no significant between-group differences in functional mobility, lower limb strength and grip strength; however, IG subjects demonstrated significantly lower fear of falling scores, and significantly higher life-space mobility and independent life-space mobility scores compared to CG subjects after the training program. Conclusions: This study demonstrates that the Backward Chaining Method is a feasible, well-tolerated intervention in a long-term care setting and it may have meaningful benefits, particularly in lessening fear of falling and improving life-space mobility and independent life-space mobility when incorporated into the usual physiotherapy interventions. Full article

Public attitudes toward immigration in Spain are influenced by media narratives, individual traits, and emotional responses. This study examines how portrayals of Arab and African immigrants may be associated with emotional and attitudinal variation. We address three questions: (1) How are different types of media coverage and social environments linked to emotional reactions? (2) What emotions are most frequently associated with these portrayals? and (3) How do political orientation and media exposure relate to changes in perception? A pre/post media exposure survey was conducted with 130 Spanish university students. Machine learning models (decision tree, random forest, and support vector machine) were used to classify attitudes and identify predictive features. Emotional variables such as fear and happiness, as well as perceptions of media clarity and bias, emerged as key features in classification models. Political orientation and prior media experience were also linked to variation in responses. These findings suggest that emotional and contextual factors may be relevant in understanding public perceptions of immigration. The use of interpretable models contributes to a nuanced analysis of media influence and highlights the value of transparent computational approaches in migration research. Full article

This study aimed to explore how social media usage influenced both parent and adolescent mental health and social identity during and after the COVID-19 pandemic through the theoretical foundational lens of social comparison theory. In-depth interviews with 24 mothers of adolescent children (ages 10–19) were conducted to address the research questions. Qualitative thematic analysis of the interview transcripts revealed eight emerging themes: (1) learning and entertainment, (2) maternal fears related to content binging and cyberbullying, (3) finding connection and comfort through social media during the pandemic, (4) ongoing digital care work as lasting maternal labor, (5) iterative dialogue: platform restrictions and content curation boundaries, (6) upward and downward social comparison, (7) fear of missing out (FoMO), and (8) third-person perception (TPP). The findings show that mothers perceive social media usage as either beneficial or harmful among adolescents (their children); upward and downward social comparison via social media exhibits more dynamic mechanisms. Moreover, this study enhances our theoretical understanding by linking social media usage to social identity, social comparison, and mental health during a global health crisis. Full article

Augmented reality is an educational technology mainly used in disciplines with a strong physical component, such as architecture or engineering. However, its application is much less common in more abstract fields, such as programming and algorithms. Some augmented reality apps for algorithm education exist, but their effect remains insufficiently assessed. In particular, emotions are an important factor for learning, and the emotional impact of augmented reality should be determined. This article inquires about the impact on students’ emotions of an augmented reality tool for learning Dijkstra’s algorithm. This investigation uses an artificial intelligence tool that detects emotions in real time through facial recognition. The data captured with this tool show that students’ positive emotions increased significantly, statistically surpassing negative emotions, and that some negative emotions, such as fear, were considerably reduced. The results show the same trend as those obtained with psychometric questionnaires, but both positive and negative emotions registered with questionnaires were significantly greater than those registered with the artificial intelligence tool. The contribution of this article is twofold. Firstly, it reinforces previous findings on the positive emotional impact of augmented reality on students. Secondly, it shows an alignment of different instruments to measure emotions, but to varying degrees. Full article

In academia, innovation intentions among students are a highly sought-after outcome due to their overarching positive impacts on performance and well-being, especially in the higher education context. This research addresses entrepreneurial education and its influence on innovation intentions across several universities in Beirut, Lebanon. The research also examines the indirect effects of opportunity recognition and psychological capital as mediators and fear of failure as a moderator. Through the lens of the theory of planned behavior, the stimulus-organism-response model, and the entrepreneurial event model, a survey was designed. A total of 263 samples were collected from the students of three universities in Beirut where the academic setting was English, and international students were present. Using Partial Least Squares—Structural Equation Modeling, the data was analyzed, and the hypotheses were supported. Results suggest that the learning environment in universities is a major determinant of innovative outcomes for students. However, implementation of entrepreneurial education alone cannot be as effective as it needs to be; it must be complemented by initiatives that enhance perceptions and internal capabilities of students to achieve innovation in their behaviors. This highlights the vitality of psychological capital and fear of failure in this context. Full article

This study introduces an innovative dog emotion classification system that integrates four non-invasive physiological indicators—skin potential (SP), muscle potential (MP), respiration frequency (RF), and voice pattern (VP)—with the extreme gradient boosting (XGBoost) algorithm. A four-breed dataset was meticulously constructed by recording and labeling physiological signals from dogs exposed to four fundamental emotional states: happiness, sadness, fear, and anger. Comprehensive feature extraction (time-domain, frequency-domain, nonlinearity) was conducted for each signal modality, and inter-emotional variance was analyzed to establish discriminative patterns. Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. Notably, this is the first study to integrate a fusion of two complementary electrophysiological indicators—skin and muscle potentials—into a multi-modal dataset for canine emotion recognition. Further interpretability analysis using Shapley Additive exPlanations (SHAP) revealed skin potential and voice pattern features as the most contributive to model performance. The proposed system demonstrates high accuracy, efficiency, and portability, laying a robust groundwork for future advancements in cross-species affective computing and intelligent animal welfare technologies. Full article

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

In the swimming context, practitioners show difficulties in learning its basic skills, and the emotional factor seems to be one of the triggers for these complications, with “fear” standing out as one of the most studied emotions due to its cognitive reactive nature associated with survival mechanisms. This emotional response can hinder the learning process in swimming, potentially leading to disengagement or dropout. The present study aimed to analyze the causes that lead to fear of the aquatic environment, its effects on learning swimming, and how swimming coaches can intervene to help overcome this fear. Direct observation was used to capture the individuals’ perception of the degree of fear. Subsequently, semi-structured interviews were conducted to analyze an intervention aimed at reducing the fear of water, followed by a corresponding content analysis. The fear of water is commonly associated with anxiety, panic, and muscle tension. The role of the swimming instructor is crucial, as their teaching approach significantly influences the swimmer’s emotional response, particularly in fostering a sense of security. The use of playful activities proves effective in helping children adapt, overcoming the limitations posed by the fear of water. Recognizing students’ fears allows instructors to structure swimming lessons effectively, helping students overcome their emotional barriers. Therefore, introducing children to the aquatic environment at an early age contributes to this goal. Full article

Cryptocurrency markets are characterized by high volatility, nonlinear dependencies, and limited transparency, making short-term forecasting particularly challenging for both researchers and practitioners. To address these complexities, this study introduces a Temporal Fusion Transformer (TFT)-based forecasting framework that integrates on-chain and technical indicators to improve predictive performance and inform tactical trading decisions. By combining multi-source features—such as Spent Output Profit Ratio (SOPR), Total Value Locked (TVL), active addresses (AA), exchange net flow (ENF), Realized Cap HODL Waves, and the Crypto Fear and Greed Index—with classical signals like Relative Strength Index (RSI) and moving average convergence divergence (MACD), the model captures behavioral patterns, investor sentiment, and price dynamics in a unified structure. Five major cryptocurrencies—BTC, ETH, USDT, XRP, and BNB—serve as the empirical basis for evaluation. The proposed TFT model is benchmarked against LSTM, GRU, SVR, and XGBoost using standard regression metrics to assess forecasting accuracy. Beyond prediction, a signal-based trading strategy is developed by translating model outputs into daily buy, hold, or sell signals, with performance assessed through a comprehensive set of financial metrics. The results suggest that integrating attention-based deep learning with domain-informed indicators provides an effective and interpretable approach for multi-asset cryptocurrency forecasting and real-time portfolio strategy optimization. Full article

The underreporting of gender-based violence (GBV) in institutions of higher learning can be attributed to a range of causes and has an impact on students’ physical and mental health. Institutions of higher learning have made efforts to eradicate the problem, yet incidences are still on the rise, calling for urgent attention. This paper focuses on the causes of the underreporting of GBV in higher education institutions (HEIs) as a point of reference to understanding the root magnitude of the pandemic in order to devise problem-specific interventions to eradicate GBV in institutions of higher learning. The rational choice theory and cultural acceptance of violence theory guided the analysis of the findings discussed in this paper. The rational choice theory provides insights into why victims choose not to report their victimization. The cultural acceptance of violence theory highlights how cultural norms can normalize and perpetuate GBV, creating barriers for victims to come forward. The findings discussed in this paper emanate from a qualitative study that gathered data using 22 one-on-one interviews with students and one focus group comprising seven supporting staff members from the University of Umvoti. Data were thematically analyzed to address the research objectives. The findings indicate that intimidation and distrust in law enforcement agents and institutions are the main reasons why GBV is underreported. Other factors that may be at play include fear of the perpetrator taking revenge, fear of not being believed, stigma and shame, the patriarchy, reliance on money, and a lack of awareness about GBV. To address these issues, this paper recommends that higher education institutions should uphold the principles of justice, fairness, and transparency in handling GBV cases. Moreover, there should be ongoing facilitation of awareness campaigns on GBV covering issues of consent, gender equality, safety, and reporting and support. When victims of GBV feel supported, they are more likely to trust the institution and report any victimization. Full article

Background/Objectives: Clinical incidents can be valuable learning tools to improve patient safety. However, failure to report or underreporting of clinical incidents is a global phenomenon. Understanding nurses’ experiences is essential to identifying challenges and developing strategies to enhance incident reporting behaviours. This review aimed to explore the experiences and perceptions of acute care bedside nurses regarding incident reporting. Methods: This review used scoping review methods. A search of the MEDLINE and CINAHL databases returned 16 papers that were included in the review. Results: Five main themes were identified—Fear of Reporting, Levels of Reporting, Lack of Knowledge, Education and Training on Reporting, Benefits of Reporting, and Changing the Culture. Conclusions: Nurses experience fear of incident reporting stemming from negative repercussions and the organisational blame culture. Lack of knowledge and training about errors and incident reporting processes limits incident reporting behaviours. To enhance reporting behaviours, promoting a just culture that includes the support of managers, open communication, and feedback on incidents is important. Education and training can also enhance nurses’ awareness and capability of incident reporting. Full article

Background: Vaccine hesitancy (VH) is a significant public health challenge, especially during the COVID-19 pandemic. Despite extensive research on the psychological and socio-political determinants of VH, its psychophysiological mechanisms remain unexplored. Grounded in the Somatic Marker Hypothesis, this study aims to investigate the neurophysiological and affective processes underlying VH. Methods: Two experiments will assess sensorimotor resonance and affective processes in VH. In the first experiment, motor-evoked potentials (MEPs) will be recorded from the deltoid and extensor carpi radialis muscles while participants view images of people receiving COVID-19 and influenza vaccines, as well as blood injections (Block 1), and images of vial containing the same substances (Block 2). Facial electromyographic (EMG) activity will measure disgust and fear responses. In the second experiment, skin conductance response (SCR) will be recorded during a virtual reality-based fear conditioning and extinction paradigm. Expected Outcomes: We hypothesize that vaccine-hesitant individuals will exhibit altered sensorimotor resonance, higher affective responses to vaccination stimuli, and impaired fear extinction learning. Psychological traits such as disgust sensitivity, paranoia, anxiety, and dogmatism are expected to be associated with VH. Conclusions: By identifying the psychophysiological mechanisms of VH, this study will contribute to developing effective vaccine promotion strategies to address future public health emergencies. Full article

There is a close relationship between self-esteem and emotions. However, most studies have relied on self-report measures, which primarily capture retrospective and generalized emotional tendencies, rather than spontaneous, momentary emotional expressions in real-time social interactions. Given that self-esteem also shapes how individuals regulate and express emotions in social contexts, it is crucial to examine whether and how self-esteem manifests in dynamic emotional expressions during self-presentation. In this study, we recorded the performances of 211 participants during a public self-presentation task using a digital video camera and measured their self-esteem scores with the Rosenberg Self-Esteem Scale. Facial Action Units (AUs) scores were extracted from each video frame using OpenFace, and four basic emotions—happiness, sadness, disgust, and fear—were quantified based on the basic emotion theory. Time-series analysis was then employed to capture the multidimensional dynamic features of these emotions. Finally, we applied machine learning and explainable AI to identify which dynamic emotional features were closely associated with self-esteem. The results indicate that all four basic emotions are closely associated with self-esteem. Therefore, this study introduces a new perspective on self-esteem assessment, highlighting the potential of nonverbal behavioral indicators as alternatives to traditional self-report measures. Full article

While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that may trigger psychological resistance in patients. Here, we propose a method based on dynamic intra-class clustering (DICC) to optimize the class imbalance problem in facial expression recognition tasks. The DICC method dynamically adjusts the distribution of majority classes by clustering them into subclasses and generating pseudo-labels, which helps the model learn more discriminative features and improve classification accuracy. By comparing with existing methods, we demonstrate that the DICC method can help the model achieve superior performance across various facial expression datasets. In this study, we conducted an in-depth evaluation of the DICC method against baseline methods using the FER2013, MMAFEDB, and Emotion-Domestic datasets, achieving improvements in classification accuracy of 1.73%, 1.97%, and 5.48%, respectively. This indicates that the DICC method can effectively enhance classification precision, especially in the recognition of minority class samples. This approach provides a novel perspective for addressing the class imbalance challenge in facial expression recognition and offers a reference for future research and applications in related fields. Full article