Search Results (83)

Alexithymia has been associated with deficits in social cognition, although findings are inconsistent and often limited by methodological constraints. This study aimed to clarify this relationship using ecologically valid and traditional standardized measures across multiple social-cognitive domains. A total of 163 adults from the general population completed a series of measures, including the Toronto Alexithymia Scale (TAS-20), Questionnaire of Cognitive and Affective Empathy (QCAE), Reading the Mind in the Eyes Test (RMET), Movies for the Assessment of Social Cognition (MASC), and Amsterdam Dynamic Facial Expression Set—Bath Intensity Variations (ADFES-BIV). Results of hierarchical regression analyses revealed that alexithymia facets significantly predicted performance on affective and cognitive empathy (QCAE), and Theory of Mind (MASC total and “No ToM” scores). The only exceptions were affective Theory of Mind (RMET) and recognition of others’ emotions (ADFES-BIV), for which none of the alexithymia facets emerged as significant predictors. The findings suggest that alexithymia is associated with poorer performance in cognitive and affective empathy and contextual Theory of Mind, whereas no significant association emerged for emotion recognition. The results suggest that integrating dynamic and context-rich tasks may be useful for detecting subtle social-cognitive difficulties in individuals with alexithymic traits. Full article

Facial expression recognition is a pivotal area in computer vision, traditionally focusing on categorical labels such as ‘Happy’, and ‘Sad’. Recent advancements have transitioned towards using the continuous indicators valence and arousal, reflecting the complicated nature of human emotions. This study introduces a method using semi-supervised learning to generate valence and arousal labels for existing categorical datasets, addressing challenges like racial bias. We propose a pseudo-label refinement framework, VAP-Refine (Valence-Arousal Pseudo-label Refinement), which enhances facial expression recognition by combining teacher model predictions and category-level statistics. These predicted labels are adjusted using ground truth category information and combined with actual category labels to train a more robust facial expression recognition model. Our approach improved accuracy from 69.3% to 72.26% and from 62.94% to 67.24% across two datasets. Fine-tuning with a teacher model predicting valence and arousal achieved 75.82% and 91.58% accuracy, with an F1-score of 0.9147, despite data imbalances. These results highlight the potential of semi-supervised learning to enhance facial expression recognition by incorporating continuous emotional indicators, improving model performance and contributing to more accurate affective computing applications. Furthermore, the proposed framework consistently improved performance across various backbone architectures, including ResNet50, SHViT, and DDAMFN++, highlighting its generalizability and versatility. Full article

Driver emotion recognition constitutes a fundamental pillar of intelligent cockpit systems, playing a pivotal role in enhancing driving safety and optimizing human–machine interaction. Despite the integration of vehicle sensor data in recent multimodal approaches, conventional fusion paradigms frequently encounter performance degradation due to the inherent noise and weak semantic correlation between vehicle telemetry and emotional states. To address these challenges, this study introduces a Dimensional Emotion-Guided Multi-task (DEGM) framework, a novel architecture designed to explicitly formalize the asymmetric roles of visual and vehicular modalities. Rather than employing simplistic feature concatenation, the proposed method maps multivariate vehicle data into a continuous Valence–Arousal–Dominance (VAD) space to characterize latent emotional tendencies within specific driving contexts. These predicted dimensions subsequently serve as semantic priors to conditionally modulate global facial representations through a Feature-wise Linear Modulation (FiLM) mechanism, facilitating robust and interpretable cross-modal interaction. Furthermore, the framework adopts a multi-task learning strategy that jointly optimizes discrete emotion classification and continuous dimension regression, leveraging the latter as a structural regularizer to refine the latent feature space. Comprehensive evaluations on the public PPB driving emotion dataset demonstrate that the proposed DEGM achieves a competitive accuracy of 87.50% and a weighted F1-score of 0.8727. The results validate that our framework provides a lightweight and robust paradigm for context-aware affect sensing, demonstrating strong potential for practical deployment in intelligent transportation systems. Full article

Emotions are fundamental to the human experience and are increasingly analyzed in applications such as marketing, healthcare, and human–computer interaction. Many recent approaches to human emotion recognition rely on deep learning, which typically demands large labeled datasets and substantial computational resources and often suffers from limited interpretability. Applying classical machine-learning methods to sensor time series is more lightweight but may struggle to reach high accuracy, especially when the temporal structure is not explicitly modelled. This paper introduces three subinterval voting-based ensemble models designed for user-specific emotion classification from multimodal time-series data acquired by smartwatch inertial sensors and heart-rate measurements. Each model partitions a time window into subwindows and performs window-level voting, thereby exploiting the temporal consistency of emotional responses while remaining compatible with standard classifiers such as logistic regression and Random Forests (with or without hyperparameter tuning). The models are evaluated on a public smartwatch emotion benchmark dataset under both binary (happy vs. sad) and three-class (happy, sad, neutral) settings. The relative accuracy improvement over the corresponding baseline reported in prior work ranges from 4.68% to 26.05%, with a mean gain of 12.34%. For the three-class tasks, improvements range from 11.17% to 37.10%, with a mean gain of 21.63%. Within the evaluated experimental setting, these results show that the proposed subinterval ensembles consistently enhance performance while remaining model-agnostic and compatible with standard user-specific classification pipelines in sensor-based emotion recognition. Full article

Background: Well-being in the elderly is shaped by complex emotional and social factors. Early identification of individuals at risk for reduced well-being may support timely preventive or supportive interventions. This study examined whether emotional intelligence indicators collected at baseline can predict well-being status 5 months later using explainable machine learning models. Methods: A cohort of elderly participants aged 60 to 89 years completed emotional intelligence measures at baseline, and well-being was assessed 5 months later using the POMS questionnaire. Four machine learning algorithms, Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were developed using 5-fold stratified cross-validation. Model performance was evaluated through accuracy, precision, recall, F1-score, ROC AUC, and normalized confusion matrices. SHapley Additive exPlanations (SHAP) were applied to interpret the contribution and directionality of each predictor. Results: XGBoost achieved the highest predictive performance (accuracy = 0.789; F1 = 0.778) and demonstrated balanced classification across well-being categories. SVM also performed robustly (accuracy = 0.760), while LR showed reduced sensitivity for detecting those with poorer well-being. SHAP analysis identified self-control, emotionality, sociability, self-motivation, and well-being components as the most influential predictors. Lower emotionality, higher sociability, and higher self-control scores were linked to a greater probability of favorable well-being outcomes. Conclusions: The findings demonstrate the feasibility of using explainable machine learning models to predict 5-month well-being status within this sample of older adults using emotional intelligence indicators. XGBoost provided the strongest and most balanced performance, while SHAP analysis clarified how specific emotional intelligence dimensions influenced predictions. These findings suggest that interpretable machine learning approaches may support future efforts toward early recognition of older adults who may be at risk for reduced well-being and guide personalized intervention strategies. Full article

Emotion recognition from physiological signals has immense applications in healthcare and human–computer interaction. We developed an electrodermal activity (EDA)-graph signal processing pipeline that produces highly sensitive features for detecting the affective dimensions (arousal and valence) of emotions. Using the Continuously Annotated Signals of Emotion dataset, we compared our graph-based EDA features (EDA-graph) with traditional time- and frequency-domain EDA features and features derived from other signals (heart rate variability, pulse transit time, electromyography, skin temperature, and respiration) for detecting affective dimensions using machine learning regression models. The EDA-graph features showed superior performance in continuous affective dimension recognition compared to the most accurate state-of-the-art models, achieving RMSE values of 0.801 for arousal and 0.714 for valence. Furthermore, we used a variety of traditional and recently published datasets collected in laboratory and ambulatory settings to perform a comprehensive evaluation of the robust generalization capabilities of our approach across different emotional contexts. The models demonstrated exceptional performance in classifying emotional states across the datasets, achieving 98.2% accuracy in detecting positive, negative, and mixed emotions; 92.75% in discriminating between emotions (relaxed, amused, bored, scared, and neutral); and 86.54% in detecting stress vs. no stress. These results highlight the potential of a graph-based analysis of EDA in emotion recognition systems in different contexts, especially for real-world applications. Full article

Background: This study examined age-related differences and interrelationships among psychological symptoms, personality traits, and emotional expression styles in a community sample of 151 participants aged 10–77 years, spanning four age groups: adolescents, young adults, middle-aged adults, and older adults. Methods: Psychological symptoms were assessed using the SCL-90, personality traits using the Big Five Inventory-2 (BFI-2), and emotional expression patterns were derived from facial expression recognition via a convolutional neural network (CNN) model. Kruskal–Wallis H tests were used to examine age-related differences. K-means cluster analysis was applied to identify emotional expression patterns, and logistic regression was used to construct a mental health risk screening model. Results: The young adult group (19–35 years) achieved the highest scores on the depression (M = 1.73) and anxiety (M = 1.61) dimensions, indicating a higher level of psychological distress during this life stage. Personality traits showed a significant developmental trajectory: neuroticism decreased with age (H(3) = 17.09, p < 0.001, η² = 0.11), declining from 2.69 in the young adult group to 2.17 in the older adult group; conscientiousness increased with age (H(3) = 37.39, p < 0.001, η² = 0.24), representing the most substantial age-related effect. K-means clustering identified three distinct emotional expression patterns: Cluster 1 was characterised by happiness, Cluster 2 by anger, disgust, and fear, and Cluster 3 by neutrality, sadness, and surprise. Cluster 2 exhibited the highest scores on neuroticism, anxiety, depression, and mood swings, and scored significantly higher than the other two clusters on interpersonal sensitivity, depression, anxiety, and hostility (p < 0.05). Mental health risk screening indicated that 26.5% of participants were classified as high-risk. Logistic regression analysis (AUC = 0.742) showed that neuroticism was the strongest predictor of elevated mental health risk (OR = 4.58), while extraversion (OR = 0.41) and conscientiousness (OR = 0.57) were significant protective factors. Conclusions: These findings provide exploratory evidence regarding age-related patterns of psychological symptoms and personality traits in a convenience sample and offer preliminary support for personality-based mental health risk screening. Notably, the SCL-90 was employed as a screening tool rather than for clinical diagnosis. Given the unequal age group sizes, particularly the small young adult subgroup, generalisability across the lifespan should not be assumed. Full article

Burnout among long-term care workers is a public health concern, yet mixed-methods evidence from China is scarce. To examine multilevel correlates of burnout, a convergent mixed-methods study using a Social–Ecological Framework was conducted. In the quantitative strand, 494 workers were surveyed using two-stage cluster sampling, and probability-weighted multivariable linear regression examined factors associated with emotional exhaustion, depersonalization, and reduced personal accomplishment. In the qualitative strand, 15 participants completed semi-structured interviews; transcripts were managed in MAXQDA 2025 and analyzed thematically. Burnout was common (30.77% mild, 33.00% moderate, 17.00% severe). Quantitative findings showed that burnout dimensions were associated with gender, age, marital status, employment arrangement, institution type, training intensity, caregiver burden, and recognition of the long-term care insurance policy (p < 0.05). Qualitative findings highlighted cognitive adaptation, emotional reciprocity with older adults, organizational training and support, and policy recognition as potential buffering resources. These findings suggest that burnout is shaped by influences across multiple levels. Coordinated efforts may help alleviate burnout by strengthening training systems, reducing caregiving burden, enhancing recognition of long-term care policies, and elevating the societal value of care work. Future research should validate these potential courses of action through longitudinal or intervention studies. Full article

Sick leave in physically demanding warehouse logistics poses persistent challenges for employee well-being, operational performance, and sustainable work participation. This study investigates how warehouse employees and supervisors understand drivers of absence and presence, and which workplace resources are perceived as most important for sustaining work happiness and attendance. Using an explanatory sequential mixed-methods design, phase 1 comprised in-depth interviews with warehouse leaders and focus groups with employees (N = 20). Qualitative findings highlight physical strain and sustained pace demands, but also emphasized psychosocial drivers such as emotional exhaustion, limited recognition, insufficient relational support, and a “push-through” culture that normalized strain and hindered recovery. At the same time, collegial support, humor, and everyday recognition were described as critical resources for coping and maintaining presence. Building on these insights, we used a cross-sectional survey (N = 99) to assess work happiness and perceived negative workplace conditions. Exploratory factor analysis identified four work happiness dimensions—supervisor support and recognition; self-development, meaning and autonomy; interpersonal relationships; and collaboration to achieve goals and four dimensions of negative workplace conditions: structural alienation, work-related exhaustion, adverse social climate, and work intensity. Multiple regression analyses showed that interpersonal relationships were the most consistent protective resource, negatively associated with exhaustion, adverse social climate, and work intensity, while supervisor support and recognition primarily reduced structural alienation. Overall, the findings suggest that social relationships constitute a central resource for sustainable well-being and attendance in physically demanding work, offering actionable implications for HRM. Full article

Background/Objectives: Teacher burnout represents a complex cognitive-emotional syndrome characterized by the interplay between mental exhaustion and emotional dysregulation, threatening educational sustainability during crisis periods. This study employed comprehensive behavioral data analysis to investigate burnout syndrome patterns among Greek teachers during the COVID-19 educational crisis, aiming to identify risk factors and resilience patterns through multiple analytical approaches that capture the syndrome’s multidimensional nature. Methods: A cross-sectional study examined primary and secondary school teachers in Western Greece during the autumn of 2021. Stratified random sampling ensured representativeness across school levels, geographic locations, and employment types. Participants completed the Greek-adapted Maslach Burnout Inventory for Educators, which measured emotional exhaustion, depersonalization, and personal accomplishment. Behavioral data analysis integrated traditional statistical methods with advanced pattern recognition techniques, including classification trees for non-linear relationships, association analysis for behavioral patterns, and cluster analysis for profile identification. Results: The majority of teachers experienced high stress with inadequate coping capabilities. Classification analysis achieved high accuracy in predicting burnout severity, identifying emotional exhaustion as the primary predictor. Deputy teachers demonstrated severe cognitive-emotional strain compared to permanent colleagues across all dimensions, with dramatically reduced personal accomplishment and minimal resources. Association analysis revealed that combined low support and high workload more than doubled burnout risk. Three distinct profiles emerged: Resilient teachers, characterized by older age and permanent employment; At-Risk teachers, showing early warning signs; and Burned Out teachers, predominantly young and in precarious employment. Remote teaching, exceeding half of the workload, significantly increased strain. Multiple regression confirmed emotional exhaustion as the dominant syndrome predictor. Conclusions: Behavioral data analysis revealed complex cognitive-emotional patterns constituting burnout syndrome during educational crisis. Employment precarity emerged as the fundamental vulnerability factor, with young deputy teachers facing dramatically higher syndrome probability compared to supported senior permanent teachers. The syndrome manifests through cascading processes where cognitive overload triggers emotional exhaustion, subsequently reducing personal accomplishment. These findings provide an evidence-based framework for early syndrome identification and targeted interventions addressing both cognitive and emotional dimensions of teacher burnout. Full article

This study investigates the evolution of public sentiment and discourse surrounding artificial intelligence through a comprehensive multi-method analysis of 28,819 Reddit comments spanning March 2015 to May 2024. Addressing three research questions—(1) what dominant topics characterize AI discourse, (2) how has sentiment changed over time, particularly following ChatGPT 5.2’s release, and (3) what linguistic patterns distinguish positive from negative discourse—we employ 28 distinct analytical techniques to provide validated insights into public AI perception. Methodologically, the study integrates VADER sentiment analysis, Linguistic Inquiry and Word Count (LIWC) analysis with regression validation, dual topic modeling using Latent Dirichlet Allocation and Non-negative Matrix Factorization for cross-validation, four-dimensional tone analysis, named entity recognition, emotion detection, and advanced NLP techniques including sarcasm detection, stance classification, and toxicity analysis. A key methodological contribution is the validation of LIWC categories through linear regression (R² = 0.049, p < 0.001) and logistic regression (61% accuracy), moving beyond the descriptive statistics typical of prior linguistic analyses. Results reveal a pronounced decline in positive sentiment from +0.320 in 2015 to +0.053 in 2024. Contrary to expectations, sentiment decreased following ChatGPT’s November 2022 release, with negative comments increasing from 31.9% to 35.1%—suggesting that direct exposure to powerful AI capabilities intensifies rather than alleviates public concerns. LIWC regression analysis identified negative emotion words (β = −0.083) and positive emotion words (β = +0.063) as the strongest sentiment predictors, confirming that affective rather than technical engagement drives public AI attitudes. Topic modeling revealed nine coherent themes, with facial recognition, algorithmic bias, AI ethics, and social media misinformation emerging as dominant concerns across both LDA and NMF analyses. Network analysis identified regulation as a central hub (degree centrality = 0.929) connecting all major AI concerns, indicating strong public appetite for governance frameworks. These findings contribute to theoretical understandings of technology risk perception, provide practical guidance for AI developers and policymakers, and demonstrate validated computational methods for tracking public opinion toward emerging technologies. Full article

The relationship between hormonal reactivity to acute stress and memory is well established, but the role of anticipatory cortisol and dehydroepiandrosterone (DHEA) levels remains underexplored. This study aimed to assess the psychobiological responses (anxiety, affect, cortisol and DHEA) to an academic examination, subsequent memory performance and associations between anticipatory hormonal response and memory retrieval. Seventy-nine undergraduates (10 males) completed an acquisition session involving picture encoding and immediate free recall. Forty-eight hours later, during the recall session, they sat a written examination followed by delayed free recall and recognition tasks. Results showed higher anticipatory anxiety, negative affect and cortisol levels in the recall session than in the acquisition session. Participants showed poorer delayed recall performance and reduced recognition of neutral pictures. In addition, after correction for multiple comparisons, exploratory hierarchical regression analyses indicated that anticipatory cortisol levels and the cortisol/DHEA ratio assessed prior to the recall session were negatively associated with total delayed free recall performance, with the cortisol/DHEA ratio also being negatively associated with delayed free recall of negative pictures. In the absence of a control group, these findings cannot be used to make causal inferences. However, they are consistent with theoretical accounts of DHEA’s anti-glucocorticoid role and highlight associations between cortisol/DHEA balance and delayed free recall performance, particularly for negative emotional material. Full article

Background and Objectives: Low back pain (LBP) is one of the most frequent complications during pregnancy, with a high and variable incidence. LBP has been associated with physical inactivity, but it has not been evaluated exclusively in physically active (PA) pregnant women. This study aimed T to estimate the incidence of LBP in PA pregnant women and describe its clinical, functional, emotional, and occupational impact. Materials and Methods: A prospective cohort of 147 women with PA pregnancies was recruited between gestational weeks 11 and 13⁺⁶. Most (92.5%) hold a university degree. All received standardized informational intervention based on international recommendations on PA during pregnancy and LBP prevention. Data were collected through an in-person interview in the first trimester and a postpartum follow-up phone interview. PA was assessed using the International Physical Activity Questionnaire (IPAQ, short version), and LBP intensity was evaluated using the Visual Analog Scale (VAS). Results: LBP occurred in 64.6% of participants, despite maintaining regular PA. Pain intensity was higher in standing position (VAS = 4.9) and lower in lateral decubitus (VAS = 2.7). More than half (55.8%) did not seek medical consultation. LBP was associated with functional limitations (work, sleep, walking), emotional distress (52.6%), and work leave (30.5%; mean 9.4 weeks). In the multivariable logistic regression analysis, standing occupational position showed a borderline association with LBP (OR = 2.14; 95% CI: 1.00–4.55; p = 0.047), while a history of LBP in a previous pregnancy showed a statistically significant association (OR = 2.89; 95% CI: 1.12–7.48; p = 0.029). Higher PA levels during pregnancy were associated with slightly lower odds of LBP (OR = 0.91 per 500 MET·min/week; 95% CI: 0.83–0.99; p = 0.032), although the magnitude of this association was small. Conclusions: LBP showed a high incidence even among PA and highly educated pregnant women. More than half of the women did not seek medical consultation, suggesting potential under-recognition of LBP. Standing occupational position and a previous pregnancy-related LBP were identified as independent risk factors associated with LBP in the multivariable model. Higher PA levels were inversely associated with LBP. Full article

Fundamental frequency estimation, also known as F0 estimation, is a crucial task in speech processing and analysis, with significant applications in areas such as speech recognition, speaker identification, and emotion detection. Traditional algorithms, while effective, often encounter challenges in real-time environments due to computational limitations. Recent advances in deep learning, especially in the use of recurrent neural networks (RNNs), have opened new opportunities for enhancing F0 estimation accuracy and efficiency. This paper introduces a novel RNN-based F0 estimation method with an attention mechanism and evaluates its performance against selected state-of-the-art F0 estimation approaches, including standard baseline methods, as well as neural-network-based regression and classification models. By integrating attention mechanisms, the model eliminates the necessity for post-processing steps and enables a more efficient seq2scal estimation process. While the self-attention mechanism used in Transformers captures all pairwise temporal dependencies at a quadratic computational cost, the proposed method’s implementation of the attention mechanism enables it to selectively focus on the most relevant acoustic cues for F0 prediction, enhancing robustness without increasing the model’s complexity. Experimental results using the LibriSpeech and Common Voice datasets demonstrate superior computational efficiency of the proposed method compared to current state-of-the-art RNN-based seq2seq models, while maintaining comparable estimation accuracy. Furthermore, the proposed “RNN-based F0 estimation method with an attention mechanism” achieves the lowest computational complexity among all compared models, while maintaining high accuracy, making it suitable for low-latency, resource-limited deployments and competitive even with standard baseline methods, such as pYIN or CREPE. Finally, the performance of the developed RNN-based F0 estimation method with attention mechanism in terms of RMSE and FLOPs demonstrates the potential of attention mechanisms and sequence modelling in achieving high accuracy alongside lightweight F0 estimation suitable for modern speech processing applications, which aligns with the growing trend towards deploying intelligent systems on resource-constrained devices. Full article

Stress, workload, and uncertainty characterize occupational tasks across sports, healthcare, military, and transportation domains. Emerging theory and empirical research suggest that coordinated whole-body movements may reflect these transient mental states. Wearable sensors and optical motion capture offer opportunities to quantify such movement dynamics and classify mental states that influence occupational performance and human–machine interaction. We tested this possibility in a small pilot study (N = 10) designed to test feasibility and identify preliminary movement features linked to mental states. Participants performed a perceptual decision-making task involving facial emotion recognition (i.e., deciding whether depicted faces were happy versus angry) with variable levels of stress (via a risk of electric shock), workload (via time pressure), and uncertainty (via visual degradation of task stimuli). The time series of movement trajectories was analyzed both holistically (full trajectory) and by phase: lowered (early), raising (middle), aiming (late), and face-to-face (sequential). For each epoch, up to 3844 linear and non-linear features were extracted across temporal, spectral, probability, divergence, and fractal domains. Features were entered into a repeated 10-fold cross-validation procedure using 80/20 train/test splits. Feature selection was conducted with the T-Rex Selector, and selected features were used to train a scikit-learn pipeline with a Robust Scaler and a Logistic Regression classifier. Models achieved mean ROC AUC scores as high as 0.76 for stress classification, with the highest sensitivity during the full movement trajectory and middle (raise) phases. Classification of workload and uncertainty states was less successful. These findings demonstrate the potential of movement-based sensing to infer stress states in applied settings and inform future human–machine interface development. Full article