Analysis and use of the emotional context with wearable devices for games and intelligent assistants

In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors

Modelling human emotions using immersive virtual reality, physiological signals and behavioural responses

Tesis por compendio[ES] El uso de la realidad virtual (RV) se ha incrementado notablemente en la comunidad científica para la investigación del comportamiento humano. En particular, la RV inmersiva ha crecido debido a la democratización de las gafas de realidad virtual o head mounted displays (HMD), que ofrecen un alto rendimiento con una inversión económica. Uno de los campos que ha emergido con fuerza en la última década es el Affective Computing, que combina psicofisiología, informática, ingeniería biomédica e inteligencia artificial, desarrollando sistemas que puedan reconocer emociones automáticamente. Su progreso es especialmente importante en el campo de la investigación del comportamiento humano, debido al papel fundamental que las emociones juegan en muchos procesos psicológicos como la percepción, la toma de decisiones, la creatividad, la memoria y la interacción social. Muchos estudios se han centrado en intentar obtener una metodología fiable para evocar y automáticamente identificar estados emocionales, usando medidas fisiológicas objetivas y métodos de aprendizaje automático. Sin embargo, la mayoría de los estudios previos utilizan imágenes, audios o vídeos para generar los estados emocionales y, hasta donde llega nuestro conocimiento, ninguno de ellos ha desarrollado un sistema de reconocimiento emocional usando RV inmersiva. Aunque algunos trabajos anteriores sí analizan las respuestas fisiológicas en RV inmersivas, estos no presentan modelos de aprendizaje automático para procesamiento y clasificación automática de bioseñales. Además, un concepto crucial cuando se usa la RV en investigación del comportamiento humano es la validez: la capacidad de evocar respuestas similares en un entorno virtual a las evocadas por el espacio físico. Aunque algunos estudios previos han usado dimensiones psicológicas y cognitivas para comparar respuestas entre entornos reales y virtuales, las investigaciones que analizan respuestas fisiológicas o comportamentales están mucho menos extendidas. Según nuestros conocimientos, este es el primer trabajo que compara entornos físicos con su réplica en RV, empleando respuestas fisiológicas y algoritmos de aprendizaje automático y analizando la capacidad de la RV de transferir y extrapolar las conclusiones obtenidas al entorno real que se está simulando. El objetivo principal de la tesis es validar el uso de la RV inmersiva como una herramienta de estimulación emocional usando respuestas psicofisiológicas y comportamentales en combinación con algoritmos de aprendizaje automático, así como realizar una comparación directa entre un entorno real y virtual. Para ello, se ha desarrollado un protocolo experimental que incluye entornos emocionales 360º, un museo real y una virtualización 3D altamente realista del mismo museo. La tesis presenta novedosas contribuciones del uso de la RV inmersiva en la investigación del comportamiento humano, en particular en lo relativo al estudio de las emociones. Esta ayudará a aplicar metodologías a estímulos más realistas para evaluar entornos y situaciones de la vida diaria, superando las actuales limitaciones de la estimulación emocional que clásicamente ha incluido imágenes, audios o vídeos. Además, en ella se analiza la validez de la RV realizando una comparación directa usando una simulación altamente realista. Creemos que la RV inmersiva va a revolucionar los métodos de estimulación emocional en entornos de laboratorio. Además, su sinergia junto a las medidas fisiológicas y las técnicas de aprendizaje automático, impactarán transversalmente en muchas áreas de investigación como la arquitectura, la salud, la evaluación psicológica, el entrenamiento, la educación, la conducción o el marketing, abriendo un nuevo horizonte de oportunidades para la comunidad científica. La presente tesis espera contribuir a caminar en esa senda.[EN] In recent years the scientific community has significantly increased its use of virtual reality (VR) technologies in human behaviour research. In particular, the use of immersive VR has grown due to the introduction of affordable, high performance head mounted displays (HMDs). Among the fields that has strongly emerged in the last decade is affective computing, which combines psychophysiology, computer science, biomedical engineering and artificial intelligence in the development of systems that can automatically recognize emotions. The progress of affective computing is especially important in human behaviour research due to the central role that emotions play in many background processes, such as perception, decision-making, creativity, memory and social interaction. Several studies have tried to develop a reliable methodology to evoke and automatically identify emotional states using objective physiological measures and machine learning methods. However, the majority of previous studies used images, audio or video to elicit emotional statements; to the best of our knowledge, no previous research has developed an emotion recognition system using immersive VR. Although some previous studies analysed physiological responses in immersive VR, they did not use machine learning techniques for biosignal processing and classification. Moreover, a crucial concept when using VR for human behaviour research is validity: the capacity to evoke a response from the user in a simulated environment similar to the response that might be evoked in a physical environment. Although some previous studies have used psychological and cognitive dimensions to compare responses in real and virtual environments, few have extended this research to analyse physiological or behavioural responses. Moreover, to our knowledge, this is the first study to compare VR scenarios with their real-world equivalents using physiological measures coupled with machine learning algorithms, and to analyse the ability of VR to transfer and extrapolate insights obtained from VR environments to real environments. The main objective of this thesis is, using psycho-physiological and behavioural responses in combination with machine learning methods, and by performing a direct comparison between a real and virtual environment, to validate immersive VR as an emotion elicitation tool. To do so we develop an experimental protocol involving emotional 360º environments, an art exhibition in a real museum, and a highly-realistic 3D virtualization of the same art exhibition. This thesis provides novel contributions to the use of immersive VR in human behaviour research, particularly in relation to emotions. VR can help in the application of methodologies designed to present more realistic stimuli in the assessment of daily-life environments and situations, thus overcoming the current limitations of affective elicitation, which classically uses images, audio and video. Moreover, it analyses the validity of VR by performing a direct comparison using highly-realistic simulation. We believe that immersive VR will revolutionize laboratory-based emotion elicitation methods. Moreover, its synergy with physiological measurement and machine learning techniques will impact transversely in many other research areas, such as architecture, health, assessment, training, education, driving and marketing, and thus open new opportunities for the scientific community. The present dissertation aims to contribute to this progress.[CA] L'ús de la realitat virtual (RV) s'ha incrementat notablement en la comunitat científica per a la recerca del comportament humà. En particular, la RV immersiva ha crescut a causa de la democratització de les ulleres de realitat virtual o head mounted displays (HMD), que ofereixen un alt rendiment amb una reduïda inversió econòmica. Un dels camps que ha emergit amb força en l'última dècada és el Affective Computing, que combina psicofisiologia, informàtica, enginyeria biomèdica i intel·ligència artificial, desenvolupant sistemes que puguen reconéixer emocions automàticament. El seu progrés és especialment important en el camp de la recerca del comportament humà, a causa del paper fonamental que les emocions juguen en molts processos psicològics com la percepció, la presa de decisions, la creativitat, la memòria i la interacció social. Molts estudis s'han centrat en intentar obtenir una metodologia fiable per a evocar i automàticament identificar estats emocionals, utilitzant mesures fisiològiques objectives i mètodes d'aprenentatge automàtic. No obstant això, la major part dels estudis previs utilitzen imatges, àudios o vídeos per a generar els estats emocionals i, fins on arriba el nostre coneixement, cap d'ells ha desenvolupat un sistema de reconeixement emocional mitjançant l'ús de la RV immersiva. Encara que alguns treballs anteriors sí que analitzen les respostes fisiològiques en RV immersives, aquests no presenten models d'aprenentatge automàtic per a processament i classificació automàtica de biosenyals. A més, un concepte crucial quan s'utilitza la RV en la recerca del comportament humà és la validesa: la capacitat d'evocar respostes similars en un entorn virtual a les evocades per l'espai físic. Encara que alguns estudis previs han utilitzat dimensions psicològiques i cognitives per a comparar respostes entre entorns reals i virtuals, les recerques que analitzen respostes fisiològiques o comportamentals estan molt menys esteses. Segons els nostres coneixements, aquest és el primer treball que compara entorns físics amb la seua rèplica en RV, emprant respostes fisiològiques i algorismes d'aprenentatge automàtic i analitzant la capacitat de la RV de transferir i extrapolar les conclusions obtingudes a l'entorn real que s'està simulant. L'objectiu principal de la tesi és validar l'ús de la RV immersiva com una eina d'estimulació emocional usant respostes psicofisiològiques i comportamentals en combinació amb algorismes d'aprenentatge automàtic, així com realitzar una comparació directa entre un entorn real i virtual. Per a això, s'ha desenvolupat un protocol experimental que inclou entorns emocionals 360º, un museu real i una virtualització 3D altament realista del mateix museu. La tesi presenta noves contribucions de l'ús de la RV immersiva en la recerca del comportament humà, en particular quant a l'estudi de les emocions. Aquesta ajudarà a aplicar metodologies a estímuls més realistes per a avaluar entorns i situacions de la vida diària, superant les actuals limitacions de l'estimulació emocional que clàssicament ha inclòs imatges, àudios o vídeos. A més, en ella s'analitza la validesa de la RV realitzant una comparació directa usant una simulació altament realista. Creiem que la RV immersiva revolucionarà els mètodes d'estimulació emocional en entorns de laboratori. A més, la seua sinergia al costat de les mesures fisiològiques i les tècniques d'aprenentatge automàtic, impactaran transversalment en moltes àrees de recerca com l'arquitectura, la salut, l'avaluació psicològica, l'entrenament, l'educació, la conducció o el màrqueting, obrint un nou horitzó d'oportunitats per a la comunitat científica. La present tesi espera contribuir a caminar en aquesta senda.Marín Morales, J. (2020). Modelling human emotions using immersive virtual reality, physiological signals and behavioural responses [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/148717TESISCompendi

Shared User Interfaces of Physiological Data: Systematic Review of Social Biofeedback Systems and Contexts in HCI

As an emerging interaction paradigm, physiological computing is increasingly being used to both measure and feed back information about our internal psychophysiological states. While most applications of physiological computing are designed for individual use, recent research has explored how biofeedback can be socially shared between multiple users to augment human-human communication. Reflecting on the empirical progress in this area of study, this paper presents a systematic review of 64 studies to characterize the interaction contexts and effects of social biofeedback systems. Our findings highlight the importance of physio-temporal and social contextual factors surrounding physiological data sharing as well as how it can promote social-emotional competences on three different levels: intrapersonal, interpersonal, and task-focused. We also present the Social Biofeedback Interactions framework to articulate the current physiological-social interaction space. We use this to frame our discussion of the implications and ethical considerations for future research and design of social biofeedback interfaces.Comment: [Accepted version, 32 pages] Clara Moge, Katherine Wang, and Youngjun Cho. 2022. Shared User Interfaces of Physiological Data: Systematic Review of Social Biofeedback Systems and Contexts in HCI. In CHI Conference on Human Factors in Computing Systems (CHI'22), ACM, https://doi.org/10.1145/3491102.351749

Affective Computing for Emotion Detection using Vision and Wearable Sensors

The research explores the opportunities, challenges, limitations, and presents advancements in computing that relates to, arises from, or deliberately influences emotions (Picard, 1997). The field is referred to as Affective Computing (AC) and is expected to play a major role in the engineering and development of computationally and cognitively intelligent systems, processors and applications in the future. Today the field of AC is bolstered by the emergence of multiple sources of affective data and is fuelled on by developments under various Internet of Things (IoTs) projects and the fusion potential of multiple sensory affective data streams. The core focus of this thesis involves investigation into whether the sensitivity and specificity (predictive performance) of AC, based on the fusion of multi-sensor data streams, is fit for purpose? Can such AC powered technologies and techniques truly deliver increasingly accurate emotion predictions of subjects in the real world? The thesis begins by presenting a number of research justifications and AC research questions that are used to formulate the original thesis hypothesis and thesis objectives. As part of the research conducted, a detailed state of the art investigations explored many aspects of AC from both a scientific and technological perspective. The complexity of AC as a multi-sensor, multi-modality, data fusion problem unfolded during the state of the art research and this ultimately led to novel thinking and origination in the form of the creation of an AC conceptualised architecture that will act as a practical and theoretical foundation for the engineering of future AC platforms and solutions. The AC conceptual architecture developed as a result of this research, was applied to the engineering of a series of software artifacts that were combined to create a prototypical AC multi-sensor platform known as the Emotion Fusion Server (EFS) to be used in the thesis hypothesis AC experimentation phases of the research. The thesis research used the EFS platform to conduct a detailed series of AC experiments to investigate if the fusion of multiple sensory sources of affective data from sensory devices can significantly increase the accuracy of emotion prediction by computationally intelligent means. The research involved conducting numerous controlled experiments along with the statistical analysis of the performance of sensors for the purposes of AC, the findings of which serve to assess the feasibility of AC in various domains and points to future directions for the AC field. The AC experiments data investigations conducted in relation to the thesis hypothesis used applied statistical methods and techniques, and the results, analytics and evaluations are presented throughout the two thesis research volumes. The thesis concludes by providing a detailed set of formal findings, conclusions and decisions in relation to the overarching research hypothesis on the sensitivity and specificity of the fusion of vision and wearables sensor modalities and offers foresights and guidance into the many problems, challenges and projections for the AC field into the future

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders

Affective state recognition in Virtual Reality from electromyography and photoplethysmography using head-mounted wearable sensors.

The three core components of Affective Computing (AC) are emotion expression recognition, emotion processing, and emotional feedback. Affective states are typically characterized in a two-dimensional space consisting of arousal, i.e., the intensity of the emotion felt; and valence, i.e., the degree to which the current emotion is pleasant or unpleasant. These fundamental properties of emotion can not only be measured using subjective ratings from users, but also with the help of physiological and behavioural measures, which potentially provide an objective evaluation across users. Multiple combinations of measures are utilised in AC for a range of applications, including education, healthcare, marketing, and entertainment. As the uses of immersive Virtual Reality (VR) technologies are growing, there is a rapidly increasing need for robust affect recognition in VR settings. However, the integration of affect detection methodologies with VR remains an unmet challenge due to constraints posed by the current VR technologies, such as Head Mounted Displays. This EngD project is designed to overcome some of the challenges by effectively integrating valence and arousal recognition methods in VR technologies and by testing their reliability in seated and room-scale full immersive VR conditions. The aim of this EngD research project is to identify how affective states are elicited in VR and how they can be efficiently measured, without constraining the movement and decreasing the sense of presence in the virtual world. Through a three-years long collaboration with Emteq labs Ltd, a wearable technology company, we assisted in the development of a novel multimodal affect detection system, specifically tailored towards the requirements of VR. This thesis will describe the architecture of the system, the research studies that enabled this development, and the future challenges. The studies conducted, validated the reliability of our proposed system, including the VR stimuli design, data measures and processing pipeline. This work could inform future studies in the field of AC in VR and assist in the development of novel applications and healthcare interventions

Fear Classification using Affective Computing with Physiological Information and Smart-Wearables

Mención Internacional en el título de doctorAmong the 17 Sustainable Development Goals proposed within the 2030 Agenda and adopted by all of the United Nations member states, the fifth SDG is a call for action to effectively turn gender equality into a fundamental human right and an essential foundation for a better world. It includes the eradication of all types of violence against women. Focusing on the technological perspective, the range of available solutions intended to prevent this social problem is very limited. Moreover, most of the solutions are based on a panic button approach, leaving aside the usage and integration of current state-of-the-art technologies, such as the Internet of Things (IoT), affective computing, cyber-physical systems, and smart-sensors. Thus, the main purpose of this research is to provide new insight into the design and development of tools to prevent and combat Gender-based Violence risky situations and, even, aggressions, from a technological perspective, but without leaving aside the different sociological considerations directly related to the problem. To achieve such an objective, we rely on the application of affective computing from a realist point of view, i.e. targeting the generation of systems and tools capable of being implemented and used nowadays or within an achievable time-frame. This pragmatic vision is channelled through: 1) an exhaustive study of the existing technological tools and mechanisms oriented to the fight Gender-based Violence, 2) the proposal of a new smart-wearable system intended to deal with some of the current technological encountered limitations, 3) a novel fear-related emotion classification approach to disentangle the relation between emotions and physiology, and 4) the definition and release of a new multi-modal dataset for emotion recognition in women. Firstly, different fear classification systems using a reduced set of physiological signals are explored and designed. This is done by employing open datasets together with the combination of time, frequency and non-linear domain techniques. This design process is encompassed by trade-offs between both physiological considerations and embedded capabilities. The latter is of paramount importance due to the edge-computing focus of this research. Two results are highlighted in this first task, the designed fear classification system that employed the DEAP dataset data and achieved an AUC of 81.60% and a Gmean of 81.55% on average for a subjectindependent approach, and only two physiological signals; and the designed fear classification system that employed the MAHNOB dataset data achieving an AUC of 86.00% and a Gmean of 73.78% on average for a subject-independent approach, only three physiological signals, and a Leave-One-Subject-Out configuration. A detailed comparison with other emotion recognition systems proposed in the literature is presented, which proves that the obtained metrics are in line with the state-ofthe- art. Secondly, Bindi is presented. This is an end-to-end autonomous multimodal system leveraging affective IoT throughout auditory and physiological commercial off-theshelf smart-sensors, hierarchical multisensorial fusion, and secured server architecture to combat Gender-based Violence by automatically detecting risky situations based on a multimodal intelligence engine and then triggering a protection protocol. Specifically, this research is focused onto the hardware and software design of one of the two edge-computing devices within Bindi. This is a bracelet integrating three physiological sensors, actuators, power monitoring integrated chips, and a System- On-Chip with wireless capabilities. Within this context, different embedded design space explorations are presented: embedded filtering evaluation, online physiological signal quality assessment, feature extraction, and power consumption analysis. The reported results in all these processes are successfully validated and, for some of them, even compared against physiological standard measurement equipment. Amongst the different obtained results regarding the embedded design and implementation within the bracelet of Bindi, it should be highlighted that its low power consumption provides a battery life to be approximately 40 hours when using a 500 mAh battery. Finally, the particularities of our use case and the scarcity of open multimodal datasets dealing with emotional immersive technology, labelling methodology considering the gender perspective, balanced stimuli distribution regarding the target emotions, and recovery processes based on the physiological signals of the volunteers to quantify and isolate the emotional activation between stimuli, led us to the definition and elaboration of Women and Emotion Multi-modal Affective Computing (WEMAC) dataset. This is a multimodal dataset in which 104 women who never experienced Gender-based Violence that performed different emotion-related stimuli visualisations in a laboratory environment. The previous fear binary classification systems were improved and applied to this novel multimodal dataset. For instance, the proposed multimodal fear recognition system using this dataset reports up to 60.20% and 67.59% for ACC and F1-score, respectively. These values represent a competitive result in comparison with the state-of-the-art that deal with similar multi-modal use cases. In general, this PhD thesis has opened a new research line within the research group under which it has been developed. Moreover, this work has established a solid base from which to expand knowledge and continue research targeting the generation of both mechanisms to help vulnerable groups and socially oriented technology.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: David Atienza Alonso.- Secretaria: Susana Patón Álvarez.- Vocal: Eduardo de la Torre Arnan

Robust Head Mounted Wearable Eye Tracking System for Dynamical Calibration

In this work, a new head mounted eye tracking system is presented. Based on computer vision techniques, the system integrates eye images and head movement, in real time, performing a robust gaze point tracking. Nystagmus movements due to vestibulo-ocular reflex are monitored and integrated. The system proposed here is a strongly improved version of a previous platform called HATCAM, which was robust against changes of illumination conditions. The new version, called HAT-Move, is equipped with accurate inertial motion unit to detect the head movement enabling eye gaze even in dynamical conditions. HAT-Move performance is investigated in a group of healthy subjects in both static and dynamic conditions, i.e. when head is kept still or free to move. Evaluation was performed in terms of amplitude of the angular error between the real coordinates of the fixed points and those computed by the system in two experimental setups, specifically, in laboratory settings and in a 3D virtual reality (VR) scenario. The achieved results showed that HAT-Move is able to achieve eye gaze angular error of about 1 degree along both horizontal and vertical direction

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective