796 research outputs found
Digitizing arquetypal human expereience through physiological signals
The problem of capturing human experience is relevant in many application domains. In fact, the process of describing and sharing individual experience lies at the heart of human culture. This advancement came at a price of losing some of the multidimensional aspects of primary, bodily experience during its projection into the symbolic formThroughout the courses of our lives we learn a great deal of information about the world from other people's experience. Besides the ability to share utilitarian experience such as whether a particular plant is poisonous, humans have developed a sophisticated competency of social signaling that enables us to express and decode emotional experience. The natural way of sharing emotional experiences requires those who share to be co-present during this event. However, people have overcome the limitation of physical presence by creating a symbolic system of representations.Recent research in the field of affective computing has addressed the question of digitization and transmission of emotional experience through monitoring and interpretation of physiological signals. Although the outcomes of this research represent a great step forward in developing a technology that supports sharing of emotional experiences, they do not seem to help in preserving the original phenomenological experience during the aforementioned projection. This circumstance is explained by the fact that in affective computing the focus of investigation has been aimed at emotional experiences which can be consciously evaluated and described by individuals themselves. Therefore, generally speaking, applying an affective computing technique for capturing emotions of an individual is not a deeper or more precise way to project her experience into the symbolic form than asking this person to write down a description of her emotions on a piece of paper. One can say that so far the research in affective computing has aimed at delivering technology that could automate the projection but it has not considered the problem of improving the projection in order to preserve more of the multidimensional aspects of human experience.This dissertation examines whether human experience, which individuals are not able to consciously transpose into the symbolic representation, can still be captured using the techniques of affective computing.First, a theoretical framework for description of human experience which is not accessible for conscious awareness was formulated. This framework was based on the work of Carl Jung who introduced a model of a psyche that includes three levels: consciousness, the personal unconscious and the collective unconscious. Consciousness is the external layer of the psyche that consists of those thoughts and emotions which are available for one¿s conscious recollection. The personal unconscious represents a repository for all of an individual¿s feelings, memories, knowledge and thoughts that are not conscious at a given moment of time.The collective unconscious is a repository of universal modes and behaviors that are similar in all individuals. According to Jung, the collective unconscious is populated with archetypes. Archetypes are prototypical categories of objects, people and situations that existed across evolutionary time and in different cultures.Esta tesis doctoral examina si la experiencia humana, que los individuos no pueden transponer conscientemente a la representación simbólica, aún puede capturarse utilizando las técnicas de computación afectiva. Primero, se formula un marco teórico para la descripción de la experiencia humana que no es accesible para la conciencia consciente. Este marco se basó en el trabajo de Carl Jung, quien introdujo un modelo de psique que incluye tres niveles: la conciencia, el inconsciente personal y el inconsciente colectivo. Habiendo definido nuestro marco teórico, realizamos un experimento en el que se mostraron a los sujetos estÃmulos visuales y auditivos de bases de datos estandarizadas para la obtención de emociones conscientes. Aparte de los estÃmulos para las emociones conscientes, los sujetos fueron expuestos a estÃmulos que representaban el arquetipo del yo. Durante la presentación de los estÃmulos cardiovasculares se registraron las señales de los sujetos. Los resultados experimentales indicaron que las respuestas de la frecuencia cardÃaca de los participantes fueron únicas para cada categorÃa de estÃmulos, incluido el arquetÃpico. Estos hallazgos dieron impulso a realizar otro estudio en el que se examinó un espectro más amplio de experiencias arquetÃpicas. En nuestro segundo estudio, hicimos un cambio de estÃmulos visuales y auditivos a estÃmulos audiovisuales porque se esperaba que los videos fueran más eficientes en la obtención de emociones conscientes y experiencias arquetÃpicas que las imágenes fijas o los sonidos. La cantidad de arquetipos aumentó y los sujetos en general fueron estimulados a sentir ocho experiencias arquetÃpicas diferentes. También preparamos estÃmulos para emociones conscientes. En este experimento, las señales fisiológicas incluyeron actividades cardiovasculares, electrodérmicas, respiratorias y temperatura de la piel. El análisis estadÃstico sugirió que las experiencias arquetÃpicas podrÃan diferenciarse en función de las activaciones fisiológicas. Además, se construyeron varios modelos de predicción basados en los datos fisiológicos recopilados. Estos modelos demostraron la capacidad de clasificar los arquetipos con una precisión que era considerablemente más alta que el nivel de probabilidad. Como los resultados del segundo estudio sugirieron una relación positiva entre las experiencias arquetÃpicas y las activaciones de señales fisiológicas, parecÃa razonable realizar otro estudio para confirmar la generalización de nuestros hallazgos. Sin embargo, antes de comenzar un nuevo experimento, se decidió construir una herramienta que pudiera facilitar la recopilación de datos fisiológicos y el reconocimiento de experiencias arquetÃpicas, asà como de emociones conscientes. Tal herramienta nos ayudarÃa a nosotros y a otros investigadores a realizar experimentos sobre la experiencia humana. Nuestra herramienta funciona en "tablets" y admite la recopilación y el análisis de datos de sensores fisiológicos. El último estudio se realizó utilizando una metodologÃa similar al segundo experimento con varias modificaciones que tenÃan como objetivo obtener resultados más sólidos. El esfuerzo de realizar este estudio se redujo considerablemente al usar la herramienta desarrollada. Durante el experimento, sólo medimos las actividades cardiovasculares y electrodérmicas de los sujetos porque nuestros experimentos anteriores mostraron que estas dos señales contribuyeron significativamente a la clasificación de las emociones conscientes y las experiencias arquetÃpicas. El análisis estadÃstico indicó una relación significativa entre los arquetipos retratados en los videos y las respuestas fisiológicas de los sujetos. Además, utilizando métodos de minerÃa de datos, creamos modelos de predicción que fueron capaces de reconocer las experiencias arquetÃpicas con una precisión menor que en el segundo estudio, pero todavÃa considerablemente..
Digitizing arquetypal human expereience through physiological signals
The problem of capturing human experience is relevant in many application domains. In fact, the process of describing and sharing individual experience lies at the heart of human culture. This advancement came at a price of losing some of the multidimensional aspects of primary, bodily experience during its projection into the symbolic formThroughout the courses of our lives we learn a great deal of information about the world from other people's experience. Besides the ability to share utilitarian experience such as whether a particular plant is poisonous, humans have developed a sophisticated competency of social signaling that enables us to express and decode emotional experience. The natural way of sharing emotional experiences requires those who share to be co-present during this event. However, people have overcome the limitation of physical presence by creating a symbolic system of representations.Recent research in the field of affective computing has addressed the question of digitization and transmission of emotional experience through monitoring and interpretation of physiological signals. Although the outcomes of this research represent a great step forward in developing a technology that supports sharing of emotional experiences, they do not seem to help in preserving the original phenomenological experience during the aforementioned projection. This circumstance is explained by the fact that in affective computing the focus of investigation has been aimed at emotional experiences which can be consciously evaluated and described by individuals themselves. Therefore, generally speaking, applying an affective computing technique for capturing emotions of an individual is not a deeper or more precise way to project her experience into the symbolic form than asking this person to write down a description of her emotions on a piece of paper. One can say that so far the research in affective computing has aimed at delivering technology that could automate the projection but it has not considered the problem of improving the projection in order to preserve more of the multidimensional aspects of human experience.This dissertation examines whether human experience, which individuals are not able to consciously transpose into the symbolic representation, can still be captured using the techniques of affective computing.First, a theoretical framework for description of human experience which is not accessible for conscious awareness was formulated. This framework was based on the work of Carl Jung who introduced a model of a psyche that includes three levels: consciousness, the personal unconscious and the collective unconscious. Consciousness is the external layer of the psyche that consists of those thoughts and emotions which are available for one¿s conscious recollection. The personal unconscious represents a repository for all of an individual¿s feelings, memories, knowledge and thoughts that are not conscious at a given moment of time.The collective unconscious is a repository of universal modes and behaviors that are similar in all individuals. According to Jung, the collective unconscious is populated with archetypes. Archetypes are prototypical categories of objects, people and situations that existed across evolutionary time and in different cultures.Esta tesis doctoral examina si la experiencia humana, que los individuos no pueden transponer conscientemente a la representación simbólica, aún puede capturarse utilizando las técnicas de computación afectiva. Primero, se formula un marco teórico para la descripción de la experiencia humana que no es accesible para la conciencia consciente. Este marco se basó en el trabajo de Carl Jung, quien introdujo un modelo de psique que incluye tres niveles: la conciencia, el inconsciente personal y el inconsciente colectivo. Habiendo definido nuestro marco teórico, realizamos un experimento en el que se mostraron a los sujetos estÃmulos visuales y auditivos de bases de datos estandarizadas para la obtención de emociones conscientes. Aparte de los estÃmulos para las emociones conscientes, los sujetos fueron expuestos a estÃmulos que representaban el arquetipo del yo. Durante la presentación de los estÃmulos cardiovasculares se registraron las señales de los sujetos. Los resultados experimentales indicaron que las respuestas de la frecuencia cardÃaca de los participantes fueron únicas para cada categorÃa de estÃmulos, incluido el arquetÃpico. Estos hallazgos dieron impulso a realizar otro estudio en el que se examinó un espectro más amplio de experiencias arquetÃpicas. En nuestro segundo estudio, hicimos un cambio de estÃmulos visuales y auditivos a estÃmulos audiovisuales porque se esperaba que los videos fueran más eficientes en la obtención de emociones conscientes y experiencias arquetÃpicas que las imágenes fijas o los sonidos. La cantidad de arquetipos aumentó y los sujetos en general fueron estimulados a sentir ocho experiencias arquetÃpicas diferentes. También preparamos estÃmulos para emociones conscientes. En este experimento, las señales fisiológicas incluyeron actividades cardiovasculares, electrodérmicas, respiratorias y temperatura de la piel. El análisis estadÃstico sugirió que las experiencias arquetÃpicas podrÃan diferenciarse en función de las activaciones fisiológicas. Además, se construyeron varios modelos de predicción basados en los datos fisiológicos recopilados. Estos modelos demostraron la capacidad de clasificar los arquetipos con una precisión que era considerablemente más alta que el nivel de probabilidad. Como los resultados del segundo estudio sugirieron una relación positiva entre las experiencias arquetÃpicas y las activaciones de señales fisiológicas, parecÃa razonable realizar otro estudio para confirmar la generalización de nuestros hallazgos. Sin embargo, antes de comenzar un nuevo experimento, se decidió construir una herramienta que pudiera facilitar la recopilación de datos fisiológicos y el reconocimiento de experiencias arquetÃpicas, asà como de emociones conscientes. Tal herramienta nos ayudarÃa a nosotros y a otros investigadores a realizar experimentos sobre la experiencia humana. Nuestra herramienta funciona en "tablets" y admite la recopilación y el análisis de datos de sensores fisiológicos. El último estudio se realizó utilizando una metodologÃa similar al segundo experimento con varias modificaciones que tenÃan como objetivo obtener resultados más sólidos. El esfuerzo de realizar este estudio se redujo considerablemente al usar la herramienta desarrollada. Durante el experimento, sólo medimos las actividades cardiovasculares y electrodérmicas de los sujetos porque nuestros experimentos anteriores mostraron que estas dos señales contribuyeron significativamente a la clasificación de las emociones conscientes y las experiencias arquetÃpicas. El análisis estadÃstico indicó una relación significativa entre los arquetipos retratados en los videos y las respuestas fisiológicas de los sujetos. Además, utilizando métodos de minerÃa de datos, creamos modelos de predicción que fueron capaces de reconocer las experiencias arquetÃpicas con una precisión menor que en el segundo estudio, pero todavÃa considerablemente..
Multimodal Approach for Emotion Recognition Using a Formal Computational Model
International audience— Emotions play a crucial role in human-computer interaction. They are generally expressed and perceived through multiple modalities such as speech, facial expressions, physiological signals. Indeed, the complexity of emotions makes the acquisition very difficult and makes unimodal systems (i.e., the observation of only one source of emotion) unreliable and often unfeasible in applications of high complexity. Moreover the lack of a standard in human emotions modeling hinders the sharing of affective information between applications. In this paper, we present a multimodal approach for the emotion recognition from many sources of information. This paper aims to provide a multi-modal system for emotion recognition and exchange that will facilitate inter-systems exchanges and improve the credibility of emotional interaction between users and computers. We elaborate a multimodal emotion recognition method from Physiological Data based on signal processing algorithms. Our method permits to recognize emotion composed of several aspects like simulated and masked emotions. This method uses a new multidimensional model to represent emotional states based on an algebraic representation. The experimental results show that the proposed multimodal emotion recognition method improves the recognition rates in comparison to the unimodal approach. Compared to the state of art multimodal techniques, the proposed method gives a good results with 72% of correct
Digitizing arquetypal human expereience through physiological signals
Tesi en modalitat de cotutela: Universitat Politècnica de Catalunya i Eindhoven University of Technology
This dissertation was produced under Erasmus Mundus Joint Doctorate
Program in Interactive and Cognitive Environments. The research was
conducted towards a joint double PhD degree between the following partner
universities: TU/e & UPCThe problem of capturing human experience is relevant in many application domains. In fact, the process of describing and sharing individual experience lies at the heart of human culture. This advancement came at a price of losing some of the multidimensional aspects of primary, bodily experience during its projection into the symbolic formThroughout the courses of our lives we learn a great deal of information about the world from other people's experience. Besides the ability to share utilitarian experience such as whether a particular plant is poisonous, humans have developed a sophisticated competency of social signaling that enables us to express and decode emotional experience. The natural way of sharing emotional experiences requires those who share to be co-present during this event. However, people have overcome the limitation of physical presence by creating a symbolic system of representations.Recent research in the field of affective computing has addressed the question of digitization and transmission of emotional experience through monitoring and interpretation of physiological signals. Although the outcomes of this research represent a great step forward in developing a technology that supports sharing of emotional experiences, they do not seem to help in preserving the original phenomenological experience during the aforementioned projection. This circumstance is explained by the fact that in affective computing the focus of investigation has been aimed at emotional experiences which can be consciously evaluated and described by individuals themselves. Therefore, generally speaking, applying an affective computing technique for capturing emotions of an individual is not a deeper or more precise way to project her experience into the symbolic form than asking this person to write down a description of her emotions on a piece of paper. One can say that so far the research in affective computing has aimed at delivering technology that could automate the projection but it has not considered the problem of improving the projection in order to preserve more of the multidimensional aspects of human experience.This dissertation examines whether human experience, which individuals are not able to consciously transpose into the symbolic representation, can still be captured using the techniques of affective computing.First, a theoretical framework for description of human experience which is not accessible for conscious awareness was formulated. This framework was based on the work of Carl Jung who introduced a model of a psyche that includes three levels: consciousness, the personal unconscious and the collective unconscious. Consciousness is the external layer of the psyche that consists of those thoughts and emotions which are available for one¿s conscious recollection. The personal unconscious represents a repository for all of an individual¿s feelings, memories, knowledge and thoughts that are not conscious at a given moment of time.The collective unconscious is a repository of universal modes and behaviors that are similar in all individuals. According to Jung, the collective unconscious is populated with archetypes. Archetypes are prototypical categories of objects, people and situations that existed across evolutionary time and in different cultures.Esta tesis doctoral examina si la experiencia humana, que los individuos no pueden transponer conscientemente a la representación simbólica, aún puede capturarse utilizando las técnicas de computación afectiva. Primero, se formula un marco teórico para la descripción de la experiencia humana que no es accesible para la conciencia consciente. Este marco se basó en el trabajo de Carl Jung, quien introdujo un modelo de psique que incluye tres niveles: la conciencia, el inconsciente personal y el inconsciente colectivo. Habiendo definido nuestro marco teórico, realizamos un experimento en el que se mostraron a los sujetos estÃmulos visuales y auditivos de bases de datos estandarizadas para la obtención de emociones conscientes. Aparte de los estÃmulos para las emociones conscientes, los sujetos fueron expuestos a estÃmulos que representaban el arquetipo del yo. Durante la presentación de los estÃmulos cardiovasculares se registraron las señales de los sujetos. Los resultados experimentales indicaron que las respuestas de la frecuencia cardÃaca de los participantes fueron únicas para cada categorÃa de estÃmulos, incluido el arquetÃpico. Estos hallazgos dieron impulso a realizar otro estudio en el que se examinó un espectro más amplio de experiencias arquetÃpicas. En nuestro segundo estudio, hicimos un cambio de estÃmulos visuales y auditivos a estÃmulos audiovisuales porque se esperaba que los videos fueran más eficientes en la obtención de emociones conscientes y experiencias arquetÃpicas que las imágenes fijas o los sonidos. La cantidad de arquetipos aumentó y los sujetos en general fueron estimulados a sentir ocho experiencias arquetÃpicas diferentes. También preparamos estÃmulos para emociones conscientes. En este experimento, las señales fisiológicas incluyeron actividades cardiovasculares, electrodérmicas, respiratorias y temperatura de la piel. El análisis estadÃstico sugirió que las experiencias arquetÃpicas podrÃan diferenciarse en función de las activaciones fisiológicas. Además, se construyeron varios modelos de predicción basados en los datos fisiológicos recopilados. Estos modelos demostraron la capacidad de clasificar los arquetipos con una precisión que era considerablemente más alta que el nivel de probabilidad. Como los resultados del segundo estudio sugirieron una relación positiva entre las experiencias arquetÃpicas y las activaciones de señales fisiológicas, parecÃa razonable realizar otro estudio para confirmar la generalización de nuestros hallazgos. Sin embargo, antes de comenzar un nuevo experimento, se decidió construir una herramienta que pudiera facilitar la recopilación de datos fisiológicos y el reconocimiento de experiencias arquetÃpicas, asà como de emociones conscientes. Tal herramienta nos ayudarÃa a nosotros y a otros investigadores a realizar experimentos sobre la experiencia humana. Nuestra herramienta funciona en "tablets" y admite la recopilación y el análisis de datos de sensores fisiológicos. El último estudio se realizó utilizando una metodologÃa similar al segundo experimento con varias modificaciones que tenÃan como objetivo obtener resultados más sólidos. El esfuerzo de realizar este estudio se redujo considerablemente al usar la herramienta desarrollada. Durante el experimento, sólo medimos las actividades cardiovasculares y electrodérmicas de los sujetos porque nuestros experimentos anteriores mostraron que estas dos señales contribuyeron significativamente a la clasificación de las emociones conscientes y las experiencias arquetÃpicas. El análisis estadÃstico indicó una relación significativa entre los arquetipos retratados en los videos y las respuestas fisiológicas de los sujetos. Además, utilizando métodos de minerÃa de datos, creamos modelos de predicción que fueron capaces de reconocer las experiencias arquetÃpicas con una precisión menor que en el segundo estudio, pero todavÃa considerablemente...Postprint (published version
Affective Computing in the Area of Autism
The prevalence rate of Autism Spectrum Disorders (ASD) is increasing at an alarming rate (1 in 68 children). With this increase comes the need of early diagnosis of ASD, timely intervention, and understanding the conditions that could be comorbid to ASD. Understanding co-morbid anxiety and its interaction with emotion comprehension and production in ASD is a growing and multifaceted area of research. Recognizing and producing contingent emotional expressions is a complex task, which is even more difficult for individuals with ASD. First, I investigate the arousal experienced by adolescents with ASD in a group therapy setting. In this study I identify the instances in which the physiological arousal is experienced by adolescents with ASD ( have-it ), see if the facial expressions of these adolescents indicate their arousal ( show-it ), and determine if the adolescents are self-aware of this arousal or not ( know-it ). In order to establish a relationship across these three components of emotion expression and recognition, a multi-modal approach for data collection is utilized. Machine learning techniques are used to determine whether still video images of facial expressions could be used to predict Electrodermal Activity (EDA) data. Implications for the understanding of emotion and social communication difficulties in ASD, as well as future targets for intervention, are discussed. Second, it is hypothesized that a well-designed intervention technique helps in the overall development of children with ASD by improving their level of functioning. I designed and validated a mobile-based intervention designed for teaching social skills to children with ASD. I also evaluated the social skill intervention. Last, I present the research goals behind an mHealth-based screening tool for early diagnosis of ASD in toddlers. The design purpose of this tool is to help people from low-income group, who have limited access to resources. This goal is achieved without burdening the physicians, their staff, and the insurance companies
Fusion of musical contents, brain activity and short term physiological signals for music-emotion recognition
In this study we propose a multi-modal machine learning approach, combining EEG and Audio features for music emotion recognition using a categorical model of emotions. The dataset used consists of film music that was carefully created to induce strong emotions. Five emotion categories were adopted: Fear, Anger, Happy, Tender and Sad. EEG data was obtained from three male participants listening to the labeled music excerpts. Feature level fusion was adopted to combine EEG and Audio features. The results show that the multimodal system outperformed the EEG mono modal system. Additionally, we evaluated the contribution of each audio feature in the classification performance of the multimodal system. Preliminary results indicate a significant contribution of individual audio features in the classification accuracy, we also found that various audio features that noticeably contributed in the classification accuracy were also reported in previous research studying the correlation between audio features and emotion ratings using the same dataset.
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information
Metadata are associated to most of the information we produce in our daily
interactions and communication in the digital world. Yet, surprisingly,
metadata are often still catergorized as non-sensitive. Indeed, in the past,
researchers and practitioners have mainly focused on the problem of the
identification of a user from the content of a message.
In this paper, we use Twitter as a case study to quantify the uniqueness of
the association between metadata and user identity and to understand the
effectiveness of potential obfuscation strategies. More specifically, we
analyze atomic fields in the metadata and systematically combine them in an
effort to classify new tweets as belonging to an account using different
machine learning algorithms of increasing complexity. We demonstrate that
through the application of a supervised learning algorithm, we are able to
identify any user in a group of 10,000 with approximately 96.7% accuracy.
Moreover, if we broaden the scope of our search and consider the 10 most likely
candidates we increase the accuracy of the model to 99.22%. We also found that
data obfuscation is hard and ineffective for this type of data: even after
perturbing 60% of the training data, it is still possible to classify users
with an accuracy higher than 95%. These results have strong implications in
terms of the design of metadata obfuscation strategies, for example for data
set release, not only for Twitter, but, more generally, for most social media
platforms.Comment: 11 pages, 13 figures. Published in the Proceedings of the 12th
International AAAI Conference on Web and Social Media (ICWSM 2018). June
2018. Stanford, CA, US
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
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