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TITLE: A VR-based Serious Game for Studying Emotional Regulation in Adolescents ABSTRACT:We all use more or less adapted strategies to confront adverse emotional situations in our lives without being psychologically affected. Emotional Regulation (ER) strategies that we use determine the way in which we feel, express and behave. Moreover, ER strategies are particularly important in adolescents, a population in the age when the deficits of ER strategies can be linked to the appearance of numerous mental health disorders such as depression or anxiety, or disruptive behaviors. Thus, the early detection of dysfunctional ER strategies and the training in adaptive ER strategies will help us to prevent the future occurrence of possible behavioral and psychosocial disorders. In this paper, we present the GAMETEEN SYSTEM (GT-System), a novel instrument based on Virtual Reality and serious games for the assessment and training of ER strategies in adolescent population. The results of our preliminary evaluation suggest that this system is effective in training and evaluating emotional regulation strategies in the adolescent population.

PurposeThe aim of this study was to establish the concurrent validity and reliability of four different two-dimensional (2D) video-based techniques for quantifying frontal plane knee kinematics during a 40 cm double-legged drop jump.Participants and methodsA convenience sample of 16 healthy participants (nine males and seven females; age: [mean ± standard deviation] 25.5±2 years; body mass index: 24.33±2.98 kg/m2) participated in this investigation. A total of five trials during a 40 cm drop jump maneuver with a countermovement jump were used as the functional task. Four knee valgus measures, such as two different frontal plane projection angle measures, knee-to-ankle separation ratio (KASR), and knee separation distance (KSD), were measured using 2D and three-dimensional (3D) systems. To generalize to the greater population of possible evaluators, the testers performing the biomechanical analyses were three novice physical therapists. Intra- and intertester intraclass correlation coefficients (ICCs) were estimated for 2D analysis variables. ICCs were estimated for all measures between systems to determine concurrent validity of the 2D system.ResultsAll four 2D measures showed good to excellent reliability (ICC: 0.89–0.99). KASR and KSD showed excellent correlation (ICC: 0.96; 95% CI: 0.82–0.98 and ICC: 0.94; 95% CI: 0.90–0.96, respectively) with the 3D system, while both methods of frontal plane projection angle showed poor to moderate correlation (ICC: 0–0.57) with the 3D system.Conclusion2D KASR and KSD measures are cost effective, reliable, and highly correlated with the same measures using 3D techniques for the evaluation of knee valgus.

TITLE:Assessing brain activations associated with emotional regulation during virtual reality mood induction procedures ABSTRACT:Emotional Regulation Strategies are used by people to influence their emotional responses to external or internal emotional stimuli. The aim of this study is to evaluate the brain activations that are associated with the application of two different emotional regulation strategies (cognitive reappraisal and expressive suppression) during virtual reality mood induction procedures. We used Emotiv EPOC to measure the brain electrical activity of participants while sadness is induced using a virtual reality environment. We monitored 24 participants, who were distributed among three experimental groups: a control group, a cognitive reappraisal group and an expressive suppression group. In the control group, we found significant activations in several right frontal regions that are related to the induction of negative emotions'. We also found significant activations in the limbic, occipital, and parietal regions in the emotional regulation groups. These regions are related to the application of emotional regulation strategies. The results are consistent with those shown in the literature, which were obtained through clinical neuroimaging systems.

Self-organizing particle systems consist of numerous autonomous, purely reflexive agents ("particles") whose collective movements through space are determined primarily by local influences they exert upon one another. Inspired by biological phenomena (bird flocking, fish schooling, etc.), particle systems have been used not only for biological modeling, but also increasingly for applications requiring the simulation of collective movements such as computer-generated animation. In this research, we take some first steps in extending particle systems so that they not only move collectively, but also solve simple problems. This is done by giving the individual particles (agents) a rudimentary intelligence in the form of a very limited memory and a top-down, goal-directed control mechanism that, triggered by appropriate conditions, switches them between different behavioral states and thus different movement dynamics. Such enhanced particle systems are shown to be able to function effectively in performing simulated search-and-collect tasks. Further, computational experiments show that collectively moving agent teams are more effective than similar but independently moving ones in carrying out such tasks, and that agent teams of either type that split off members of the collective to protect previously acquired resources are most effective. This work shows that the reflexive agents of contemporary particle systems can readily be extended to support goal-directed problem solving while retaining their collective movement behaviors. These results may prove useful not only for future modeling of animal behavior, but also in computer animation, coordinated movement control in robotic teams, particle swarm optimization, and computer games.

Elsevier Wrzesien, M.; Rodriguez Ortega, A.; Rey, B.; Alcañiz Raya, ML.; Banos, R.; Vara, M. (2015). How the physical similarity of avatars can influence the learning of emotion regulation strategies in teenagers. Computers in Human Behavior. 43:101-111. doi:10.1016/j.chb.2014 How the physical similarity of avatars can influence the learning of emotion regulation strategies in teenagers AbstractThe aim of this study is to evaluate the influence of the physical similarity of avatars with the user on emotion regulation strategy training. In this study twenty-four teenagers observed an avatar (either physically similar to the participant or neutral) that gets frustrated with his/her computer, after which he/she applies an emotion regulation strategy (slow breathing). The intensity of the emotional induction and regulation processes was measured using questionnaires and electroencephalogram data. The results show that observing an avatar that is physically similar to the participant has a significantly greater impact on emotional valence and arousal in participants and also induces emotional states that are significantly more intense than when observing a neutral avatar. The results seem to indicate significantly greater activation of specific brain regions that are related to these processes and greater identification with the avatar in terms of both subjective and objective measures in participants that observed an avatar that was physically similar to them. However, there were no significant differences in the sense of presence or the appeal (i.e., satisfaction) to participants regarding the virtual environment. The use of avatars in mental health applications is relatively new and its specific influence is still unknown. We consider this study to be a first step forward in better understanding the use of avatars in mental health applications for youth. This research brings new guidelines to the design of different types of applications in this field in order to achieve greater behavioral changes in youth.

Early control of fruit quality requires reliable and rapid determination techniques. Therefore, the food industry has a growing interest in non-destructive methods such as spectroscopy. The aim of this study was to evaluate the feasibility of visible and nearinfrared (NIR) spectroscopy, in combination with multivariate analysis techniques, to predict the level and changes of astringency in intact and in the flesh of half cut persimmon fruits. The fruits were harvested and exposed to different treatments with 95 % CO 2 at 20 ºC for 0, 6, 12, 18 and 24 h to obtain samples with different levels of astringency. A set of 98 fruits was used to develop the predictive models based on their spectral data and another external set of 42 fruit samples was used to validate the models. The models were created using the partial least squares regression (PLSR), support vector machine (SVM) and least squares support vector machine (LS-SVM). In general, the models with the best performance were those which included standard normal variate (SNV) in the pre-processing. The best model was the PLSR developed with SNV along with the first derivative (1-Der) pre-processing, created using the data obtained at six measurement points of the intact fruits and all wavelengths (R 2 =0.904 and RPD=3.26). Later, a successive projection algorithm (SPA) was applied to select the most effective wavelengths (EWs). Using the six points of measurement of the *Manuscript Click here to view linked References intact fruit and SNV together with the direct orthogonal signal correction (DOSC) preprocessing in the NIR spectra, 41 EWs were selected, achieving an R 2 of 0.915 and an RPD of 3.46 for the PLSR model. These results suggest that this technology has potential for use as a feasible and cost-effective method for the non-destructive determination of astringency in persimmon fruits.