Search Results (90)

Background: Children and adolescents with autism spectrum disorder (ASD) frequently receive antipsychotics and are considered at increased risk for weight gain. Few studies have compared longitudinal weight trajectories between youth with ASD and those with other psychiatric disorders. Methods: This naturalistic, registry-based study used data from the SENTIA cohort, which prospectively monitors antipsychotic safety in individuals under 18 years at a university hospital in Spain. Clinical characteristics were compared between participants with and without ASD. Longitudinal body mass index (BMI) z-score trajectories were analysed using linear mixed-effects models. Results: The sample included 266 participants, of whom 113 (42.5%) had ASD. Individuals with ASD were more often male and initiated antipsychotic treatment at a younger age. Of the 26 participants prescribed an antipsychotic before age 6, 88.5% had ASD. Comorbidity profiles were similar across groups. Risperidone and aripiprazole were the most frequently prescribed antipsychotics. BMI z-scores increased over time (β = 0.130, p = 0.017), and baseline BMI z-score was the strongest predictor. ASD diagnosis did not modify the average linear rate of BMI z-score change (time × ASD: p = 0.251); however, a significant quadratic time × ASD interaction (β = −0.016, p = 0.041) was consistent with a more pronounced early increase followed by earlier attenuation of BMI z-scores in the ASD group. Conclusions: Although antipsychotic treatment was initiated earlier in youth with ASD, no clear difference was observed in the rate of BMI z-score change. Differences in weight trajectories underscore the need for metabolic monitoring in antipsychotic-treated youth. Full article

Background: Tick-borne encephalitis (TBE) virus is transmitted to humans via tick bites and occasionally via the consumption of unpasteurized milk products. According to the literature, the most important driver of TBE emergence and increase in incidence in humans is changes in human behaviour/activities. Method and principal findings: To compensate for the lack of data, expert opinions were gathered to identify the risk factors for exposure to tick bites linked to twenty-eight human activities (professional or recreational) in forests and to target prevention messages at the populations most at risk. Opinions were elicited from a total of twenty-five European experts. Seven criteria were included in the analysis for each activity: frequency, seasonality, duration of exposure, distance covered, degree of contact with vegetation, speed and average level of protection against tick bites. The activities considered to be the most at risk of exposure to tick bites are, in descending order: three occupational activities (forest monitoring activities, forestry and wood industry activities and scientific and/or analytical activities), five recreational activities and one hunting activity (mushroom picking, spending the night in the forest, hunting, naturalist activities, orienteering, and berry or fruit picking). Conclusions and significance: Prevention messages regarding tick bites could be targeted at people who engage in activities considered in this analysis to be at highest risk of exposure to tick bites. Full article

Real-time emotion monitoring is increasingly relevant in healthcare, automotive, and workplace applications, where adaptive systems can enhance user experience and well-being. This study investigates the feasibility of classifying emotions along the valence–arousal dimensions of the Circumplex Model of Affect using EEG signals acquired from a single mastoid channel positioned near the ear. Twenty-four participants viewed emotion-eliciting videos and self-reported their affective states using the Self-Assessment Manikin. EEG data were recorded with an OpenBCI Cyton board and both spectral and temporal features (including power in multiple frequency bands and entropy-based complexity measures) were extracted from the single ear-channel. A dual analytical framework was adopted: classical statistical analyses (ANOVA, Mann–Whitney U) and artificial neural networks combined with explainable AI methods (Gradient × Input, Integrated Gradients) were used to identify features associated with valence and arousal. Results confirmed the physiological validity of single-channel ear-EEG, and showed that absolute $\beta$- and $\gamma$-band power, spectral ratios, and entropy-based metrics consistently contributed to emotion classification. Overall, the findings demonstrate that reliable and interpretable affective information can be extracted from minimal EEG configurations, supporting their potential for wearable, real-world emotion monitoring. Nonetheless, practical considerations—such as long-term comfort, stability, and wearability of ear-EEG devices—remain important challenges and motivate future research on sustained use in naturalistic environments. Full article

Mobile Global Positioning System (GPS) data offer a promising approach to inferring mental health status through behavioural analysis. Whilst previous research has explored location-based behavioural indicators including location clusters, entropy, and variance, persistent GPS measurement errors have compromised data reliability, limiting the practical deployment of smartphone-based digital phenotyping systems. This study develops and validates an algorithmic preprocessing method designed to mitigate inherent GPS measurement limitations in mobile health applications. We conducted comprehensive evaluation through controlled experimental protocols and naturalistic field assessments involving 38 participants over a seven-day period, capturing GPS data across diverse environmental contexts on both Android and iOS platforms. The proposed preprocessing algorithm demonstrated exceptional precision, consistently detecting major activity centres within an average 50-metre margin of error across both platforms. In naturalistic settings, the algorithm yielded robust location detection capabilities, producing spatial patterns that reflected plausible and behaviourally meaningful traits at the individual level. Cross-platform analysis revealed consistent performance regardless of operating system, with no significant differences in accuracy metrics between Android and iOS devices. These findings substantiate the potential of mobile GPS data as a reliable, objective source of behavioural information for mental health monitoring systems, contingent upon implementing sophisticated error-mitigation techniques. The validated algorithm addresses a critical technical barrier to the practical implementation of GPS-based digital phenotyping, enabling the more accurate assessment of mobility-related behavioural markers across diverse mental health conditions. This research contributes to the growing field of mobile health technology by providing a robust algorithmic framework for leveraging smartphone sensing capabilities in healthcare applications. Full article

Mental fatigue significantly impairs student performance and learning outcomes, yet reliable neurophysiological assessment methods remain elusive in educational research. This systematic review examines electroencephalography (EEG) as an objective monitoring tool for mental fatigue in student populations, with particular focus on portable and wearable device applications. Following PRISMA guidelines, we systematically analysed 18 empirical studies (2012–2024, N = 595 participants, ages 10–32) employing continuous EEG during educational tasks. We evaluated frequency band definitions, EEG hardware configurations (from 4-channel portable devices to 64-channel research systems), electrode placements, preprocessing pipelines, and analytical approaches, including machine learning methods. Most studies identified increased frontal theta (4–8 Hz) and decreased beta (13–30 Hz) power as primary fatigue markers across diverse EEG systems. However, substantial methodological heterogeneity emerged: frequency band definitions varied considerably, preprocessing techniques differed, and small sample sizes (median N = 20) limited statistical power. While portable EEG systems demonstrate promise for objective, non-invasive cognitive state monitoring in naturalistic educational settings, current methodological inconsistencies constrain reliability and validity. This review identifies critical standardisation gaps and provides evidence-based recommendations for wearable EEG device development and implementation, including standardised protocols, automated artifact removal strategies, and validation linking EEG measures to educational outcomes. Full article

This study investigates drivers’ visual attention strategies during naturalistic urban driving using mobile eye-tracking (Pupil Labs Neon). A sample of experienced drivers participated in a realistic traffic scenario to examine fixation behaviour under varying traffic conditions. Non-parametric analyses revealed substantial variability in fixation behaviour attributable to driver identity (H(9) = 286.06, p = 2.35 × 10⁻⁵⁶), stimulus relevance (H(7) = 182.64, p = 5.40 × 10⁻³⁶), and traffic density (H(4) = 76.49, p = 9.64 × 10⁻¹⁶). Vehicles and pedestrians elicited significantly longer fixations than lower-salience categories, reflecting adaptive allocation of visual attention to behaviourally critical elements of the scene. Compared with the fixed-rule method, which produced inflated anomaly rates of 7.23–14.84% (mean 12.06 ± 2.71%), the DSGP algorithm yielded substantially lower and more stable rates of 1.62–3.33% (mean 2.48 ± 0.53%). The fixed-rule approach over-classified anomalies by approximately 4–6×, whereas DSGP more accurately distinguished contextually appropriate fixations from genuine attentional deviations. These findings demonstrate that fixation behaviour in driving is strongly shaped by individual traits and environmental context, and that driver-specific modelling substantially improves the reliability of attention monitoring. Therefore DSGP framework offers a robust, personalised alternative evaluated at the proof-of-concept level to fixed thresholds and represents a promising direction for enhancing driver-state assessment in future ADAS. Full article

Mental effort, a critical factor influencing task performance, is often difficult to measure accurately and efficiently. Pupil diameter has emerged as a reliable, real-time indicator of mental effort. This study introduces RIPA2, an enhanced pupillometric index for real-time mental effort assessment. Building on the original RIPA method, RIPA2 incorporates refined Savitzky–Golay filter parameters to better isolate pupil diameter fluctuations within biologically relevant frequency bands linked to cognitive load. We validated RIPA2 across two distinct tasks: a structured N-back memory task and a naturalistic information search task involving fact-checking and decision-making scenarios. Our findings show that RIPA2 reliably tracks variations in mental effort, demonstrating improved sensitivity and consistency over the original RIPA and strong alignment with the established offline measures of pupil-based cognitive load indices, such as LHIPA. Notably, RIPA2 captured increased mental effort at higher N-back levels and successfully distinguished greater effort during decision-making tasks compared to fact-checking tasks, highlighting its applicability to real-world cognitive demands. These findings suggest that RIPA2 provides a robust, continuous, and low-latency method for assessing mental effort. It holds strong potential for broader use in educational settings, medical environments, workplaces, and adaptive user interfaces, facilitating objective monitoring of mental effort beyond laboratory conditions. Full article

Rapid global urbanization has intensified the demand for sensing solutions that can capture the complex interactions between urban environments and their impact on human physical and mental health. Conventional laboratory-based approaches, while offering high experimental control, often lack ecological validity and fail to represent real-world exposures. To address this gap, we present the eMOTIONAL Cities Walker—a portable multimodal sensing platform designed as a wearable backpack unit developed for the synchronous collecting of multimodal data in either indoor or outdoor settings. The system integrates a suite of environmental sensors (covering microclimate, air pollution and acoustic monitoring) with physiological sensing technologies, including electroencephalography (EEG), mobile eye-tracking and wrist-based physiological monitoring. This configuration enables real-time acquisition of environmental and physiological signals in dynamic, naturalistic settings. Here, we describe the system’s technical architecture, sensor specifications, and field deployment across selected Lisbon locations, demonstrating its feasibility and robustness in urban environments. By bridging controlled laboratory paradigms with ecologically valid real-world sensing, this platform provides a novel tool to advance translational research at the intersection of sensor technology, human experience, and urban health. Full article

Understanding and accurately estimating cognitive workload is crucial for the development of adaptive, user-centered interactive systems across a variety of domains including augmented reality, automotive driving assistance, and intelligent tutoring systems. Cognitive workload assessment enables dynamic system adaptation to improve user experience and safety. In this work, we introduce a novel framework that leverages smooth pursuit eye movements as a non-invasive and temporally precise indicator of mental effort. A key innovation of our approach is the development of trajectory-independent algorithms that address a significant limitation of existing methods, which generally rely on a predefined or known stimulus trajectory. Our framework leverages two solutions to provide accurate cognitive load estimation, without requiring knowledge of the exact target path, based on Kalman filter and B-spline heuristic classifiers. This enables the application of our methods in more naturalistic and unconstrained environments where stimulus trajectories may be unknown. We evaluated these algorithms against classical supervised machine learning models on a publicly available benchmark dataset featuring diverse pursuit trajectories and varying cognitive workload conditions. The results demonstrate competitive performance along with robustness across different task complexities and trajectory types. Moreover, our framework supports real-time inference, making it viable for continuous cognitive workload monitoring. To further enhance deployment feasibility, we propose a federated learning architecture, allowing privacy-preserving adaptation of models across heterogeneous devices without the need to share raw gaze data. This scalable approach mitigates privacy concerns and facilitates collaborative model improvement in distributed real-world scenarios. Experimental findings confirm that metrics derived from smooth pursuit eye movements reliably reflect fluctuations in cognitive states induced by working memory load tasks, substantiating their use for real-time, continuous workload estimation. By integrating trajectory independence, robust classification techniques, and federated privacy-aware learning, our work advances the state of the art in adaptive human–computer interaction. This framework offers a scientifically grounded, privacy-conscious, and practically deployable solution for cognitive workload estimation that can be adapted to diverse application contexts. Full article

Voice acoustics have been extensively investigated as potential non-invasive markers for Autism Spectrum Disorder (ASD). Although many studies report high accuracies, they typically rely on highly controlled clinical protocols that reduce linguistic variability. Their data is also recorded using specialized microphone arrays that ensure high-quality recordings. Such dependencies limit their applicability in real-world or in-home screening contexts. In this work, we explore an alternative approach designed to reflect the requirements of mobile-based applications that could assist parents in monitoring their children. We use an open-access dataset of naturalistic storytelling, extracting only the speech segments in which the child is speaking. We applied previously published ASD voice-analysis pipelines to this dataset, which yielded suboptimal performance under these less controlled conditions. We then introduce a deep learning-based method that learns discriminative representations directly from raw audio, eliminating the need for manual feature extraction while being more robust to environmental noise. This approach achieves an accuracy of up to 77% in classifying children with ASD, children with Attention Deficit Hyperactivity Disorder (ADHD), and neurotypical children. Frequency-band occlusion sensitivity analysis on the deep model revealed that ASD speech relied more heavily on the 2000–4000 Hz range, TD speech on both low (100–300 Hz) and high (4000–8000 Hz) bands, and ADHD speech on mid-frequency regions. These spectral patterns may help bring us closer to developing practical, accessible pre-screening tools for parents. Full article

The accelerating global population aging has brought increasing attention to the loneliness and emotional needs experienced by older adults due to shrinking social networks and the loss of relatives and friends, which significantly impair their quality of life and psychological well-being. In this context, companion robots powered by artificial intelligence are increasingly regarded as a scalable and sustainable form of emotional intervention that can address older people’s affective and social requirements. This study systematically reviews research trends in this field, analyzing the structure of emotional needs among older users and their acceptance mechanisms toward robot functionalities. First, a keyword co-occurrence analysis was conducted using VOSviewer on relevant literature published between 2000 and 2025 from the Web of Science database, revealing focal research topics and emerging trends. Subsequently, questionnaire surveys and in-depth interviews were carried out to identify emotional needs and functional preferences among elderly users. Findings indicate that the field is characterized by increasing interdisciplinary integration, with affective computing and naturalistic interaction becoming central concerns. Empirical results reveal significant differences in need structures across age groups: the oldest-old prioritize safety monitoring and daily assistance, whereas the young-old emphasize social interaction and developmental activities. Regarding emotional interaction, older adults generally prefer natural and non-intrusive expressive styles and exhibit reserved attitudes toward highly anthropomorphic designs. Key factors influencing acceptance include practicality, ease of use, privacy protection, and emotional warmth. The study concludes that effective companion robot design should be grounded in a nuanced understanding of the heterogeneous needs of the aging population, integrating functionality, interaction, and emotional value. Future development should emphasize adaptive and customizable capabilities, adopt natural yet restrained interaction strategies, and strengthen real-world cross-cultural and long-term evaluations. Full article

A collaborative learning approach between citizen scientists, experts and managers transformed metrics of coral reef fish biodiversity into indicators for use in regional waterway health report cards. We tested a citizen science tool, iNaturalist, to identify species and monitor annual changes in fish biodiversity at a regional scale in the Great Barrier Reef, Australia. The participation of almost 1000 citizen scientists between 2013 and 2025 resulted in 13,131 research grade observations of 684 species of fish. Annual biodiversity data from three years (2023–2025) was compared to 10 years of baseline data (2013–2022) and calibrated for effort. Report cards scores for fish ecological health were generally ‘very good’ to ‘good’ and we conclude that a citizen science methodology is potentially suitable for fish ecological health at multiple spatial and temporal scales. Full article

PostCOVID-19 is a condition affecting approximately 10% of individuals infected with SARS-CoV-2, presenting significant challenges in diagnosis and clinical management. Portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), offer real-time insights into cerebral hemodynamics and represent a promising tool for studying postCOVID-19 in naturalistic settings. This study investigates the integration of fNIRS with machine learning to identify neural correlates of postCOVID-19. A total of six machine learning classifiers—Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP)—were evaluated using a stratified subject-aware cross-validation scheme on a dataset comprising 29,737 time-series samples from 37 participants (9 postCOVID-19, 28 controls). Four different feature representation strategies were compared: raw time-series, PCA-based dimensionality reduction, statistical feature extraction, and a hybrid approach that combines time-series and statistical descriptors. Among these, the hybrid representation demonstrated the highest discriminative performance. The SVM classifier trained on hybrid features achieved strong discrimination ($\mathit{ROC}\text{-}\mathit{AUC}$ = 0.909) under subject-aware CV5; at the default threshold, $\mathit{Sensitivity}$ was moderate and $\mathit{Specificity}$ was high, outperforming all other methods. In contrast, models trained on statistical features alone exhibited limited $\mathit{Sensitivity}$ despite high $\mathit{Specificity}$. These findings highlight the importance of temporal information in the fNIRS signal and support the potential of machine learning combined with portable neuroimaging for postCOVID-19 identification. This approach may contribute to the development of non-invasive diagnostic tools to support individualized treatment and longitudinal monitoring of patients with persistent neurological symptoms. Full article

AI tools like Passive Acoustic Monitoring (PAM) and apps like iNaturalist and Merlin are increasingly used in bird monitoring and species identification. The purpose of this study was to assess whether AI-assisted binoculars improve bird species knowledge, particularly in novice birders, and to examine users’ motivation and experience. This study focuses on the learning impact of users, not data quality or accuracy of the device itself. Participants were recruited via social media, mostly novices (10 women, 9 men, 1 diverse). Four experimental groups (A–D, with N = 5 participants each) were designated. Participants used AI-supported binoculars to identify 10 bird species and the same binoculars with AI function switched off to identify another 10 bird species based on two sets of different species (counterbalanced to avoid order effects). This allowed a between-group as well as a within-subject comparison. We used a pre-test/post-test design for learning. Significant knowledge gains occurred only when using AI binoculars (Wilcoxon tests, p = 0.008). Pooled data across the intervention groups showed strong learning effects for AI-assisted users (Z = −3.736, p = 0.001). No significant learning occurred under control conditions. As a conclusion, AI-assisted binoculars significantly enhance bird identification learning in novices, but as a cautionary note, the study needs to be extended to live birds and in longitudinal settings. Full article

Animal welfare assessments increasingly aim to quantify enclosure use and activity to support naturalistic behavior and improve Quality of Life (QoL). Traditionally, this is achieved through manual observations, which are time-consuming, subject to observer bias, and limited in temporal resolution due to short observation periods. Here, we compared manual tracking using ZooMonitor with automated pose estimation (SLEAP) in a mother–son pair of black-headed spider monkeys (Ateles fusciceps) at Aalborg Zoo. We collected manual observations on six non-consecutive days (median daily duration: 62 min, mean: 66 min, range: 52–90 min) and visualized this as spatial heatmaps. We applied pose estimation to the same video footage, tracking four body parts to generate corresponding heatmaps. Across most days, the methods showed strong agreement (overlap 83–99%, Pearson’s r = 0.93–1.00), with both highlighting core activity areas on the floor near the central climbing structures and by the door with feeding gutters. Both methods also produced comparable estimates of time spent being active, with no significant difference across days (p = 0.952). Our results demonstrate that computer vision technology can provide a reliable and scalable tool for monitoring enclosure use and activity, enhancing the efficiency and consistency of zoo-based welfare assessments while reducing reliance on labor-intensive manual observations. Full article