Search Results (96)

Limb movement is an important component of control during safety-critical tasks such as driving. Restricted movement, such as limitations associated with an injury or surgery to the upper limb, may impact driving safety. However, the degree of upper limb movement required for driving is not well described outside of traditional laboratory settings. There is a need for new affordable, accessible, reliable and accurate measures of normative limb movement to guide decisions about driving capacity. This feasibility study applied a volume estimation approach to wrist-worn triaxial accelerometry data to quantify upper limb movement during naturalistic driving in a young adult population. A sample of 89 participants wore accelerometers while engaging in daily driving activity over a two-week period. Results demonstrated a distribution of movement volumes, consistent with variation in individual driving behaviour. This volume estimation approach has strong potential for further development as both a research tool and clinical assessment method, particularly in rehabilitation and return-to-driving assessments following upper limb injury or surgery. Full article

Ants are vital bioindicators that contribute to soil health and food webs, making accurate identification essential for biodiversity monitoring and conservation. However, traditional taxonomic methods are time-consuming and require specialized expertise, limiting large-scale data collection and public participation. This paper presents AntID_APP, a web-based application designed to support citizen scientists in Taiwan by enabling real-time, image-based detection and the identification of native ant genera. Fine-tuned YOLO models first detect ants in user-uploaded images and then classify them at the genus level. The models were trained on a curated dataset of 60,429 open-access images from iNaturalist, covering 54 native ant species. To ensure robustness in real-world conditions, we applied targeted data augmentation and evaluated multiple YOLO versions (v9–v12). The best-performing model achieved a mean Average Precision (mAP50: 0.935–0.948, mAP50-95: 0.777–0.807) for the detection task, followed by accurate genus-level identification. The application features an intuitive interface and a lightweight asynchronous server architecture, allowing users to upload images and receive both visual detection results (bounding boxes) and genus predictions efficiently. By combining high accuracy with accessibility, AntID_APP offers a scalable solution for biodiversity monitoring and public engagement in ecological research. Full article

Background: The quality and valence of psychedelic experiences are influenced by a range of psychological and contextual factors. This study examines baseline mood and the “relational triad”—comprising social connectedness, mindfulness, and spirituality—as potential predictors of the quality of naturalistic psychedelic experiences. Methods: Data were drawn from the Predicting Responses to Psychedelics dataset, a longitudinal study tracking 654 individuals planning to take a psychedelic substance. Participants completed self-report measures at five time points, before and after ingestion. Baseline mood (depression, anxiety, and wellbeing) and relational triad factors were assessed at Timepoint 1, while acute psychedelic experience quality was measured at Timepoint 3 using validated scales (MEQ-30, CEQ, and ASC). Results: Mystical and challenging experiences were weakly but positively correlated. Baseline depression and anxiety were predictive of more challenging experiences but not of mystical-type experiences, while baseline wellbeing predicted more mystical and less challenging experiences. Mindfulness and spirituality were positively associated with mystical experiences, while social connectedness and mindfulness were inversely associated with challenging experiences. Conclusions: These findings extend previous research by demonstrating that baseline psychological and relational factors shape the nature of psychedelic experiences. Full article

Background: Children with emotional disturbance (ED) frequently display highly unpredictable behaviors compared to other children. The magnitude and unpredictability of childhood ED make finding effective management strategies difficult for parents. Prior research has examined parents’ stress and the children’s behaviors in schools, but we know very little about how parents manage at home. Methods: This qualitative study used Naturalistic Inquiry to explore how parents respond to the challenges which arise at home due to childhood ED. Eight mothers raising 10 children with ED were recruited nationally. Data were gathered through semi-structured, individual interviews. Results: Consequences-based parenting strategies were unsuccessful, but mothers achieved greater success with pre-planned, intentional responses and adapting the child’s environment. Mothers learned their child’s world view was very different than their own. This realization caused mothers’ perspective toward their child to change. Mothers saw their child as struggling with a problem, instead of simply being defiant. The perception shift allowed mothers to approach situations with greater compassion and inner peace. Conclusions: The findings provide suggestions for pediatric healthcare providers who work with such parents seeking assistance and advice. 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

Background: Research to identify changes in driving behavior that occur with the onset of Pre-MCI and MCI is an emerging area with many gaps still to be addressed. These gaps include limited use of objective, continuous measurement of driver behavior in real-life traffic conditions and comprehensive, biomarker-validated, cognitive evaluation based upon both testing and clinical ratings. Using these strategies, the questions addressed in this exploratory study are whether or not differences in driving behavior are indicative of Pre-MCI/MCI and which behaviors are most predictive of Pre-MCI/MCI. Methods: As part of a naturalistic longitudinal study, older drivers with a Montreal Cognitive Assessment score ≥ 19 had telematic sensors installed in their vehicles and underwent comprehensive cognitive assessment quarterly for three years. Thirty-six participants were classified as Unimpaired (n = 23) or Pre-MCI/MCI (n = 10/3) based upon a neuropsychological battery and diagnostic algorithm. A penalized generalized linear mixed-effects model (GLMM) with a logistic link and LASSO regularization was used to model Pre-MCI/MCI group membership vs. unimpaired as a function of ten trip-level telematic features (trip distance, hard acceleration, hard braking, hard turns, speed average, maximum speed, RPM average, fuel level, throttle average, and throttle variability) at the end of their first 12 months in the study. Results: Higher RPM, shorter average trips, and greater throttle variability predicted higher odds of Pre-MCI/MCI, while more frequent hard braking, hard turns, higher mean speed, and lower average throttle (steadier pedal control) predicted lower odds of Pre-MCI/MCI. Conclusions: The model clearly distinguished unimpaired older drivers from those with MCI or Pre-MCI, suggesting that distinct patterns of driver behavior may be related to levels of cognitive function. 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

Background/Objectives: Parental involvement is currently recommended by Italian national guidelines on autism spectrum disorder (ASD) intervention. However, research on the impact of parent-mediated interventions on parental skills and children’s outcomes in Italy is limited. This study evaluated the feasibility of delivering Parent-ESDM (Parent-mediated Early Start Denver Model), a well-supported Naturalistic Developmental Behavioral Intervention (NDBI) known to benefit parents’ well-being and children’s development, within an Italian healthcare service. Methods: Twenty parent–child dyads participated in weekly 1 h Parent-ESDM sessions for 6 months. Spontaneous parent–child interactions were assessed at baseline, mid-intervention, and post-intervention to examine parents’ use of NDBI strategies and changes in children’s core ASD behaviors. Results: Throughout the intervention, parents acquired a good level of fidelity in the use of NDBI strategies and children obtained significant improvements in core ASD behaviors. Conclusions: These preliminary findings support the feasibility of delivering a parent-mediated intervention within an Italian healthcare service. The positive trends observed provide a strong rationale for conducting controlled trials to more definitively evaluate this model and its potential adoption as a future standard practice. Full article

With the increasing adoption of virtual reality (VR) in research and training applications, reliable stress detection in naturalistic settings remains challenging, particularly when hardware complexity must be minimized. This study presents an enhanced framework for real-time stress recognition in VR environments that integrates behavioral interactions with selectively derived physiological signals. Building upon previous architectures, the proposed framework incorporates pre-task baseline measurements to account for subject-specific and session-initial variability. While the comprehensive analysis employs a three-class affective framework, the practical implementation focuses on binary stress detection for real-world VR applications. Stress detection is achieved through VR-based behavioral signals, complemented by minimal input from a Galvanic Skin Response (GSR) sensor. The experimental evaluation demonstrates that baseline calibration improves separation across stress conditions. Quantitatively, the proposed Weighted Baseline Detector (WBD) achieved a classification accuracy of 94.17% and an Area Under the Curve (AUC) of 0.9993, outperforming the fixed global baseline approach (85.0% accuracy, AUC 0.9067), which demonstrates the effectiveness of the proposed calibration method. Rigorous cross-validation confirms that the approach achieves stable performance with statistical significance across stress conditions. These findings highlight the potential of combining behavioral analysis with physiological support to develop practical, low-hardware VR platforms for live stress recognition. Full article

Place attachment, or the emotional bond between people and physical settings, is a central concept in urban design and environmental psychology. Although biophilic and restorative environmental frameworks have stressed the value of natural environments, empirical research investigating nature and place attachment often reduces naturalness to simple greenness metrics, leaving the role of aesthetic and visual structural qualities underexplored. This study addresses this gap by drawing on empirical aesthetics and Christopher Alexander’s theory of living structures, which frames aesthetics as an underlying order that gives rise to the experience of visual coherence and beauty. We conducted a multi-method quantitative case study on ten campus open spaces, combining a student survey (n = 447), timed-interval behavioural observations, independent aesthetic ratings, and computational image analysis. The data analysis relied on correlation and regression, as well as data triangulation from multiple sources that encompassed both subjective and objective measurements. Regression and mediation models showed that perceived restorativeness was the strongest predictor of place attachment, complemented by sense of community, perceived wholeness, and naturalness. Indirect pathways revealed that passive interaction enhanced attachment through restorativeness, while active interaction did so through a sense of community. Image-based metrics, particularly fractal dimension and entropy, were closely aligned with perceptions of naturalness and restoration, while behavioural observations confirmed the distinct roles of social hubs, solitary natural retreats, and transitional spaces. The findings demonstrate that both naturalistic structure and social affordances are essential to attachment, and that living structure qualities offer a valuable framework for linking aesthetic order to restorative and emotional bonds. These insights provide both theoretical enrichment and practical guidance for designing restorative and life-enhancing public environments. Full article

Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8–13 Hz) reliably indexes emotional valence with 75–85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal–midline theta (4–8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85–98% accuracy for subject identification and 70–95% for state classification. However, significant challenges persist: spatial resolution remains limited (2–3 cm), inter-individual variability is substantial (alpha peak frequency: 7–14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective–cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain–computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration. Full article

Citizen science is not only a participatory means of contributing to scientific knowledge but also an effective approach to addressing a wide range of societal challenges. Integrating citizen science with sustainability entails leveraging public engagement in scientific research to promote sustainable practices and advance the United Nations 2030 Agenda for Sustainable Development Goals (SDGs). The degree of public participation can influence the learning outcomes achieved. This study investigated the benefits and limitations of a co-creation citizen science approach implemented in a school context for monitoring species distribution on rocky shores, aligned with SDGs 4, 13, and 14. A mixed-methods design was applied, combining questionnaires administered to students (n = 100); participant observations of students, teachers, and researchers; and the analysis of observations submitted by one class (C2) to the iNaturalist platform. Students recorded 21 valid observations representing 13 different taxa, and developed skills such as critical thinking, problem-solving, collaboration, and interpersonal communication. They also recognised the potential of co-creation as a means of addressing scientific questions. However, teachers reported constraints in implementing the project, notably the breadth of the school curriculum and the lack of local support. This study reinforces the potential of co-creation citizen science projects to foster sustainable education. Full article

Multisensory behavioral research is increasingly aiming to move beyond traditional laboratories and into real-world settings. Smartphones offer a promising platform for this purpose, but their use in psychophysical experiments requires rigorous validation of their ability to precisely present multisensory stimuli and record reaction times (RTs). To date, no study has systematically assessed the feasibility of conducting RT-based multisensory paradigms on smartphones. In this study, we developed a reproducible validation method to quantify smartphones’ temporal precision in synchronized auditory–tactile stimulus delivery and RT logging. Applying this method to five Android devices, we identified two with sufficient precision. We also introduced a technique to enhance RT measurement by combining touchscreen and accelerometer data, effectively doubling the measure resolution—from 8.33 ms (limited by a 120 Hz refresh rate) to 4 ms. Using a top-performing device identified through our validation, we conducted an audio–tactile RT experiment with 20 healthy participants. Looming sounds were presented through headphones during a tactile detection task. Results showed that looming sounds reduced tactile RTs by 20–25 ms compared to static sounds, replicating a well-established multisensory effect linked to peripersonal space. These findings present a robust method for validating smartphones for cognitive research and demonstrate that high-precision audio–tactile paradigms can be reliably implemented on mobile devices. This work lays the groundwork for rigorous, scalable, and ecologically valid multisensory behavioral studies in naturalistic environments, expanding participant reach and enhancing the relevance of multisensory research. Full article

Background: There is an emerging need for new scalable behavioral assays, i.e., assays that are feasible to administer from the comfort of the person’s home, with ease and at higher frequency than clinical visits or visits to laboratory settings can afford us today. This need poses several challenges which we address in this work along with scalable solutions for behavioral data acquisition and analyses aimed at diversifying various populations under study here and to encourage citizen-driven participatory models of research and clinical practices. Methods: Our methods are centered on the biophysical fluctuations unique to the person and on the characterization of behavioral states using standardized biorhythmic time series data (from kinematic, electrocardiographic, voice, and video-based tools) in naturalistic settings, outside a laboratory environment. The methods are illustrated with three representative studies (58 participants, 8–70 years old, 34 males, 24 females). Data is presented across the nervous systems under a proposed functional taxonomy that permits data organization according to nervous systems’ maturation and decline levels. These methods can be applied to various research programs ranging from clinical trials at home, to remote pedagogical settings. They are aimed at creating new standardized biometric scales to screen and diagnose neurological disorders across the human lifespan. Results: Using this remote data collection system under our new unifying statistical platform for individualized behavioral analysis, we characterize the digital ranges of biophysical signals of neurotypical participants and report departure from normative ranges in neurodevelopmental and neurodegenerative disorders. Each study provides parameter spaces with self-emerging clusters whereby data points corresponding to a cluster are probability distribution parameters automatically classifying participants into different continuous Gamma probability distribution families. Non-parametric analysis reveals significant differences in distributions’ shape and scale (p < 0.01). Data reduction is realizable from full probability distribution families to a single parameter, the Gamma scale, amenable to represent each participant within each subclass, and each cluster of similar participants within each cohort. We report on data integration from stochastic analyses that serve to differentiate participants and propose new ways to highly scale our research, education, and clinical practices. Conclusions: This work highlights important methodological and analytical techniques for developing personalized and scalable biometrics across various populations outside a laboratory setting. Full article