22 research outputs found

    EMOTIONS RECOGNITION IN VIDEO GAME PLAYERS USING PHYSIOLOGICAL INFORMATION

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    Video games are interactive software able to arouse different kinds of emotions in players. Usually, the game designer tries to define a set of game features able to enjoy, engage, and/or educate the consumers. Through the gameplay, the narrative, and the game environment, a video game is able to interact with players' intellect and emotions. Thanks to the technological developments of the last years, the gaming industry has grown to become one of the most important entertainment markets. The scientific community and private companies have put a lot of efforts on the technical aspects as well as on the interaction aspects between the players and the video game. Considering the game design, many theories have been proposed to define some guidelines to design games able to arouse specific emotions in consumers. They mainly use interviews or observations in order to deduce the goodness of their approach through qualitative data. There are some works based on empirical studies aimed at studying the emotional states directly on players, using quantitative data. However, these researches usually consider the data analysis as a classification problem involving, mainly, the game events. Our goal is to understand how the feelings, experienced by the players, can be automatically deducted, and how these emotional states can be used to improve the game quality. In order to pursue this purpose, we have measured the mental states using physiological signals in order to return a set of quantitative values used to identify the players emotions. The most common ways to identify emotions are: to use a discrete set of labels (e.g., joy, anger), or to assess them inside an n-dimensional vector space. Albeit the most natural way to describe the emotions is to represent them through their name, the latter approach provides a quantitative result that can be used to define the new game status. In this thesis, we propose a framework aimed at an automatic assessment, using physiological data, of emotions in a 2-dimensional space, structured by valence and arousal vectors. The former may vary between pleasure and displeasure, while the latter defines the level of physiological activation. As a consequence, we have considered as most effective to infer the players\u2019 mental states, the following physiological data: electrocardiography (ECG), electromyography on 5 facial muscles (Facial EMG), galvanic skins response (GSR), and respiration intensity/rate. We have recorded a video, during a set of game sessions, of the player's face and of her gameplay. To acquire the affective information, we have shown the recorded video and audio to the player, and we have asked to self-assess her/his emotional state over the entire game on the valence and arousal vectors presented above. Starting from this framework, we have conducted two sets of experiments. In the first experiment, our aim was to validate the procedure. We have collected the data of 10 participants while playing at 4 platform games. We have also analyzed the data to identify the emotion pattern of the player during the gaming sessions. The analysis has been conducted in two directions: individual analysis (to find the physiological pattern of an individual player), and collective analysis (to find the generic patterns of the sample population). The goal of the second experiment has been to create a dataset of physiological information of 33 players, and to extend the data analysis and the results provided by the pilot study. We have asked the participants to play at 2 racing games in two different environments: on a standard monitor and using a head mounted display for Virtual Reality. After we have collected the information useful to the dataset creation, we have analyzed the data focusing on individual analysis. In both analyses, the self-assessment and the physiological data have been used in order to infer the emotional state of the players in each moment of the game sessions, and to build a prediction model of players' emotions using Machine Learning techniques. Therefore, the three main contributions of this thesis are: to design a novel framework for study the emotions of video game players, to develop an open-source architecture and a set of software able to acquire the physiological signals and the affective states, to create an affective dataset using racing video games as stimuli, to understand which physiological conditions could be the most relevant in order to determine the players' emotions, and to propose a method for the real-time prediction of a player's mental state during a video game session. The results to suggest that it is possible to design a model that fits with player's characteristics, predicting her emotions. It could be an effective tool available to game designers who can introduce innovative features to their games

    Alzheimer Disease Detection Techniques and Methods: A Review

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    Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

    A comparative study of signal processing methods for structural health monitoring

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    In this paper four non-parametric and five parametric signal processing techniques are reviewed and their performances are compared through application to a sample exponentially damped synthetic signal with closely-spaced frequencies representing the ambient response of structures. The non-parametric methods are Fourier transform, periodogram estimate of power spectral density, wavelet transform, and empirical mode decomposition with Hilbert spectral analysis (Hilbert-Huang transform). The parametric methods are pseudospectrum estimate using the multiple signal categorization (MUSIC), empirical wavelet transform, approximate Prony method, matrix pencil method, and the estimation of signal parameters by rotational invariance technique (ESPRIT) method. The performances of different methods are studied statistically using the Monte Carlo simulation and the results are presented in terms of average errors of multiple sample analyses

    Attention Restraint, Working Memory Capacity, and Mind Wandering: Do Emotional Valence or Intentionality Matter?

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    Attention restraint appears to mediate the relationship between working memory capacity (WMC) and mind wandering (Kane et al., 2016). Prior work has identifed two dimensions of mind wandering—emotional valence and intentionality. However, less is known about how WMC and attention restraint correlate with these dimensions. Te current study examined the relationship between WMC, attention restraint, and mind wandering by emotional valence and intentionality. A confrmatory factor analysis demonstrated that WMC and attention restraint were strongly correlated, but only attention restraint was related to overall mind wandering, consistent with prior fndings. However, when examining the emotional valence of mind wandering, attention restraint and WMC were related to negatively and positively valenced, but not neutral, mind wandering. Attention restraint was also related to intentional but not unintentional mind wandering. Tese results suggest that WMC and attention restraint predict some, but not all, types of mind wandering

    Contributions to the Modelling of Auditory Hallucinations, Social robotics, and Multiagent Systems

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    165 p.The Thesis covers three diverse lines of work that have been tackled with the central endeavor of modeling and understanding the phenomena under consideration. Firstly, the Thesis works on the problem of finding brain connectivity biomarkers of auditory hallucinations, a rather frequent phenomena that can be related some pathologies, but which is also present in healthy population. We apply machine learning techniques to assess the significance of effective brain connections extracted by either dynamical causal modeling or Granger causality. Secondly, the Thesis deals with the usefulness of social robotics strorytelling as a therapeutic tools for children at risk of exclussion. The Thesis reports on the observations gathered in several therapeutic sessions carried out in Spain and Bulgaria, under the supervision of tutors and caregivers. Thirdly, the Thesis deals with the spatio-temporal dynamic modeling of social agents trying to explain the phenomena of opinion survival of the social minorities. The Thesis proposes a eco-social model endowed with spatial mobility of the agents. Such mobility and the spatial perception of the agents are found to be strong mechanisms explaining opinion propagation and survival

    Psychological Engagement in Choice and Judgment Under Risk and Uncertainty

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    Theories of choice and judgment assume that agents behave rationally, choose the higher expected value option, and evaluate the choice consistently (Expected Utility Theory, Von Neumann, & Morgenstern, 1947). However, researchers in decision-making showed that human behaviour is different in choice and judgement tasks (Slovic & Lichtenstein, 1968; 1971; 1973). In this research, we propose that psychological engagement and control deprivation predict behavioural inconsistencies and utilitarian performance with judgment and choice. Moreover, we explore the influences of engagement and control deprivation on agent’s behaviours, while manipulating content of utility (Kusev et al., 2011, Hertwig & Gigerenzer 1999, Tversky & Khaneman, 1996) and decision reward (Kusev et al, 2013, Shafir et al., 2002)

    Contributions to the Modelling of Auditory Hallucinations, Social robotics, and Multiagent Systems

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    165 p.The Thesis covers three diverse lines of work that have been tackled with the central endeavor of modeling and understanding the phenomena under consideration. Firstly, the Thesis works on the problem of finding brain connectivity biomarkers of auditory hallucinations, a rather frequent phenomena that can be related some pathologies, but which is also present in healthy population. We apply machine learning techniques to assess the significance of effective brain connections extracted by either dynamical causal modeling or Granger causality. Secondly, the Thesis deals with the usefulness of social robotics strorytelling as a therapeutic tools for children at risk of exclussion. The Thesis reports on the observations gathered in several therapeutic sessions carried out in Spain and Bulgaria, under the supervision of tutors and caregivers. Thirdly, the Thesis deals with the spatio-temporal dynamic modeling of social agents trying to explain the phenomena of opinion survival of the social minorities. The Thesis proposes a eco-social model endowed with spatial mobility of the agents. Such mobility and the spatial perception of the agents are found to be strong mechanisms explaining opinion propagation and survival

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port
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