Search Results (10)

Immersive methods and biometric tools provide a rigorous, context-rich way to study how people perceive and choose food. Immersive methods use extended reality, including virtual, augmented, mixed, and augmented virtual environments, to recreate settings such as homes, shops, and restaurants. They increase participants’ sense of presence and the ecological validity (realism of conditions) of experiments, while still tightly controlling sensory and social cues like lighting, sound, and surroundings. Biometric tools record objective signals linked to attention, emotion, and cognitive load via sensors such as eye-tracking, galvanic skin response (GSR), heart rate (and variability), facial electromyography, electroencephalography, and functional near-infrared spectroscopy. Researchers align stimuli presentation, gaze, and physiology on a common temporal reference and link these data to outcomes like liking, choice, or willingness-to-buy. This approach reveals implicit responses that self-reports may miss, clarifies how changes in context shift perception, and improves predictive power. It enables faster, lower-risk product and packaging development, better-informed labeling and retail design, and more targeted nutrition and health communication. Good practices emphasize careful system calibration, adequate statistical power, participant comfort and safety, robust data protection, and transparent analysis. In food science and consumer behavior, combining immersive environments with biometrics yields valid, reproducible evidence about what captures attention, creates value, and drives food choice. Full article

Electoral abstention has emerged as a critical challenge to democratic legitimacy, with rising rates observed globally. For example, in Portugal, the turnout declined from 91.5% in 1975 to 51.4% in 2022. This systematic review synthesizes multidisciplinary literature to identify key determinants of voter nonparticipation and their interactions, aiming to inform adaptive strategies to enhance civic engagement amid social, organizational, and technological changes. Following PRISMA guidelines, we searched five databases (Academic Search Complete, MEDLINE, Psychology and Behavioral Sciences Collection, PsycINFO, and Web of Science) from 2000 to August 2025 using terms such as “electoral abstention” and “non-voting.” Inclusion criteria prioritized quantitative empirical studies in peer-reviewed journals in English, Portuguese, Spanish, or French, yielding 23 high-quality studies (assessed via MMAT, with scores ≥ 60%) from 13 countries, predominantly the USA and France. Results reveal abstention as a multidimensional phenomenon driven by three interconnected categories: individual factors (e.g., health issues like smoking and mental health trajectories, institutional distrust); institutional factors (e.g., electoral reforms such as biometric registration reducing abstention by up to 50% in local contexts, but with mixed outcomes in voluntary voting systems); and contextual factors (e.g., economic inequalities and urbanization correlating with lower turnout, exacerbated by events like COVID-19). This review underscores the need for integrated public policies addressing these factors to boost participation, particularly among youth and marginalized groups. By framing abstention as an adaptive response to contemporary challenges, this work contributes to the political psychology and democratic reform literature, advocating interdisciplinary approaches to resilient electoral systems. Full article

This systematic review provides an analysis of information gathered from 33 chosen publications during the past decade. The analysis reveals the primary methodologies applied and identifies the visitor behaviors that enable personalized content delivery. Statistical and Data Analysis is the predominant methodology in the reviewed publications. The methodology is present in 97% of the publications. AI and Machine Learning (63.6%) and Mobile/Interactive Technologies (60.6%) are most frequently paired with this methodology. Behavioral Analytics Platforms and Mobile/Wearable Devices are the most used technologies (42.4%) for delivering personalized content. A total of 39.4% of publications utilize Location Tracking Systems. The most frequent visitor behavior analysis focuses on Interactive Engagement and Movement Patterns, which occur 72.7% of the time, before Learning Patterns and Physical Positioning, which occur 63.6% of the time. The behavioral analysis of Group Dynamics (27.3%) and Emotional Response (18.2%) represents the least common practice when museums personalize their content despite the significance of social interaction analysis among visitors. The leading content personalization methods currently include real-time personalization systems combined with AI-driven systems and location-based technologies. Personalized content delivery systems face challenges including privacy protection and scalability issues paired with expensive implementation costs, which especially affect smaller museums. Researchers should explore how new technologies, such as virtual reality, augmented reality, and advanced biometric systems, can be integrated into future developments. Full article

Emotions fluctuate during the process of social interaction. Although the co-creation of emotions through organizational behavior has been discussed theoretically in existing research, there is no method to demonstrate how emotions are co-created. Instead, previous studies have paid much attention to empathy, in which a person’s emotions are contagious. In contrast to self-report, which is a traditional method that can only assess emotions at a single point in time and adapts to empathy, biometric technology has made it possible to analyze emotional fluctuations over time. However, previous studies have focused only on understanding the emotional fluctuations of individuals separately. In the present study, we developed a system to measure the co-creation of emotions using a wearable device. The pulse rate was converted into valence as a positive–negative emotion, and the fluctuations in valence were analyzed by cross-correlation. We demonstrated the feasibility of the proposed system through triangulation by integrating biometrics with observation and self-report. The proposed system was verified to measure the co-creation of pair and group emotions using real-world data beyond laboratory settings. The present study contributes to business administration by proposing a critical concept for measuring the co-creation of emotions based on a constructionist approach. Full article

There are limited reports of community-based nutrition education with culinary instruction that measure biomarkers, particularly in low-income and underrepresented minority populations. Teaching kitchens have been proposed as a strategy to address social determinants of health, combining nutrition education, culinary demonstration, and skill building. The purpose of this paper is to report on the development, implementation, and evaluation of Journey to Health, a program designed for community implementation using the RE-AIM planning and evaluation framework. Reach and effectiveness were the primary outcomes. Regarding reach, 507 individuals registered for the program, 310 participants attended at least one nutrition class, 110 participants completed at least two biometric screens, and 96 participants attended at least two health coaching appointments. Participants who engaged in Journey to Health realized significant improvements in body mass index, blood pressure, and triglycerides. For higher risk participants, we additionally saw significant improvements in total and LDL cholesterol. Regarding dietary intake, we observed a significant increase in cups of fruit and a decrease in sugar sweetened beverages consumed per day. Our findings suggest that Journey to Health may improve selected biometrics and health behaviors in low-income and underrepresented minority participants. Full article

The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems. Full article

Present-day smartphones provide various conveniences, owing to high-end hardware specifications and advanced network technology. Consequently, people rely heavily on smartphones for a myriad of daily-life tasks, such as work scheduling, financial transactions, and social networking, which require a strong and robust user authentication mechanism to protect personal data and privacy. In this study, we propose draw-a-deep-pattern (DDP)—a deep learning-based end-to-end smartphone user authentication method using sequential data obtained from drawing a character or freestyle pattern on the smartphone touchscreen. In our model, a recurrent neural network (RNN) and a temporal convolution neural network (TCN), both of which are specialized in sequential data processing, are employed. The main advantages of the proposed DDP are (1) it is robust to the threats to which current authentication systems are vulnerable, e.g., shoulder surfing attack and smudge attack, and (2) it requires few parameters for training; therefore, the model can be consistently updated in real-time, whenever new training data are available. To verify the performance of the DDP model, we collected data from 40 participants in one of the most unfavorable environments possible, wherein all potential intruders know how the authorized users draw the characters or symbols (shape, direction, stroke, etc.) of the drawing pattern used for authentication. Of the two proposed DDP models, the TCN-based model yielded excellent authentication performance with average values of 0.99%, 1.41%, and 1.23% in terms of AUROC, FAR, and FRR, respectively. Furthermore, this model exhibited improved authentication performance and higher computational efficiency than the RNN-based model in most cases. To contribute to the research/industrial communities, we made our dataset publicly available, thereby allowing anyone studying or developing a behavioral biometric-based user authentication system to use our data without any restrictions. Full article

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy. Full article

Wearable activity trackers have gained popularity among individuals who want to track their physical activity (PA). The features of wearable tracking technology that are known to facilitate positive behavior changes such as self-monitoring and social connectedness) are well documented; yet, the existing evidence is not conclusive in the literature requiring further investigation. This study was an 8-week pilot randomized controlled study examining the effectiveness of PA intervention incorporating a wearable activity tracker’s online connectivity feature. Forty participants were equally randomized into either an individual-based (n = 20) or a connected group (n = 20). A Jawbone UP24 tracker was provided to all participants in both groups as a means of self-monitoring PA for eight weeks, but the connected group was additionally instructed to share their PA levels with the others using the accompanying smartphone application. Participants’ weekly step counts were evaluated each week to examine the change in PA. Participants’ biometric variables such as body weight, body mass index, waist circumference, blood pressure, and psychological status, including self-efficacy (SE) and exercise motivation (EM), were measured from both groups before and after the intervention period. Additionally, the social support questionnaire (SSQ) was measured among the connected group. The statistical significance level was set at <0.05. The average step counts for eight weeks were significantly increased only in the connected group (p < 0.001). Significant differences in step count improved from the baseline to week 8 in the connected user group (p < 0.01), but only baseline vs. week 7 in the individual users. Also, no significant interaction effects for biometric variables, EM, and SE were founded. However, SSQ was significantly improved in the connected user group (p < 0.001). PA intervention combining a wearable activity tracker and online social connectivity feature shows a greater effectiveness of promoting PA than a wearable tracker alone Full article

Android is offering millions of apps on Google Play-store by the application publishers. However, those publishers do have a parent organization and share information with them. Through the ‘Android permission system’, a user permits an app to access sensitive personal data. Large-scale personal data integration can reveal user identity, enabling new insights and earn revenue for the organizations. Similarly, aggregation of Android app permissions by the app owning parent organizations can also cause privacy leakage by revealing the user profile. This work classifies risky personal data by proposing a threat model on the large-scale app permission aggregation by the app publishers and associated owners. A Google-play application programming interface (API) assisted web app is developed that visualizes all the permissions an app owner can collectively gather through multiple apps released via several publishers. The work empirically validates the performance of the risk model with two case studies. The top two Korean app owners, seven publishers, 108 apps and 720 sets of permissions are studied. With reasonable accuracy, the study finds the contact number, biometric ID, address, social graph, human behavior, email, location and unique ID as frequently exposed data. Finally, the work concludes that the real-time tracking of aggregated permissions can limit the odds of user profiling. Full article