Physiological Self Regulation with Biofeedback Games

Mental stress is a global epidemic that can have serious health consequences including cardiovascular diseases and diabetes. Several techniques are available to teach stress self-regulation skills including therapy, meditation, deep breathing, and biofeedback. While effective, these methods suffer from high drop-outs due to the monotonic nature of the exercises and are generally practiced in quiet relaxed environment, which may not transfer to real-world scenarios. To address these issues, this dissertation presents a novel intervention for stress training using games and wearable sensors. The approach consists of monitoring the user’s physiological signals during gameplay, mapping them into estimates of stress levels, and adapting the game in a way that promotes states of low arousal. This approach offers two key advantages. First, it allows users to focus on the gameplay rather than on monitoring their physiological signals, which makes the training far more engaging. More importantly, it teaches users to self-regulate their stress response, while performing a task designed to increase arousal. Within this broad framework, this dissertation studies three specific problems. First, the dissertation evaluates three physiological signals (breathing rate, heart rate variability, and electrodermal activity) that span across the dimensions of degrees of selectivity in measuring arousal and voluntary control in their effectiveness in lowering arousal. This will identify the signal appropriate for game based stress training and the associated bio-signal processing techniques for real-time arousal estimation. Second, this dissertation investigates different methods of biofeedback presentation e.g. visual feedback and game adaptation during gameplay. Selection of appropriate biofeedback mechanism is critical since it provides the necessary information to improve the perception of visceral states (e.g. stress) to the user. Furthermore, these modalities facilitate skill acquisition in distinct ways (i.e., top-down and bottom-up learning) and influence retention of skills. Third, this dissertation studies reinforcement scheduling in a game and its effect on skill learning and retention. A reinforcement schedule determines which occurrences of the target response are reinforced. This study focuses on continuous and partial reinforcement schedules in GBF and their effect on resistance to extinction (i.e. ability to retain learned skills) after the biofeedback is removed. The main contribution of this dissertation is in demonstrating that stress self-regulation training can be embedded in videogames and help individuals develop more adaptive responses to reduce physiological stress encountered both at home and work

Applications of Affective Computing in Human-Robot Interaction: state-of-art and challenges for manufacturing

The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance. In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, i.e., capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0. In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g., facial expressions, body language, voice, or physiological signals) and to adapt its behaviour accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed. The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g., healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector

Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments

Confusion is an emotion that is likely to occur while learning complex information. This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments (DLEs), where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behavior, it is possible to improve the design of interactive DLEs (IDLEs) not only in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report and behavioral and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed

Enhancing attention in autism spectrum disorder: comparative analysis of virtual reality-based training programs using physiological data

BackgroundThe ability to maintain attention is crucial for achieving success in various aspects of life, including academic pursuits, career advancement, and social interactions. Attention deficit disorder (ADD) is a common symptom associated with autism spectrum disorder (ASD), which can pose challenges for individuals affected by it, impacting their social interactions and learning abilities. To address this issue, virtual reality (VR) has emerged as a promising tool for attention training with the ability to create personalized virtual worlds, providing a conducive platform for attention-focused interventions. Furthermore, leveraging physiological data can be instrumental in the development and enhancement of attention-training techniques for individuals.MethodsIn our preliminary study, a functional prototype for attention therapy systems was developed. In the current phase, the objective is to create a framework called VR-PDA (Virtual Reality Physiological Data Analysis) that utilizes physiological data for tracking and improving attention in individuals. Four distinct training strategies such as noise, score, object opacity, and red vignette are implemented in this framework. The primary goal is to leverage virtual reality technology and physiological data analysis to enhance attentional capabilities.ResultsOur data analysis results revealed that reinforcement training strategies are crucial for improving attention in individuals with ASD, while they are not significant for non-autistic individuals. Among all the different strategies employed, the noise strategy demonstrates superior efficacy in training attention among individuals with ASD. On the other hand, for Non-ASD individuals, no specific training proves to be effective in enhancing attention. The total gazing time feature exhibited benefits for participants with and without ASD.DiscussionThe results consistently demonstrated favorable outcomes for both groups, indicating an enhanced level of attentiveness. These findings provide valuable insights into the effectiveness of different strategies for attention training and emphasize the potential of virtual reality (VR) and physiological data in attention training programs for individuals with ASD. The results of this study open up new avenues for further research and inspire future developments

Learning Biosignals with Deep Learning

The healthcare system, which is ubiquitously recognized as one of the most influential system in society, is facing new challenges since the start of the decade.The myriad of physiological data generated by individuals, namely in the healthcare system, is generating a burden on physicians, losing effectiveness on the collection of patient data. Information systems and, in particular, novel deep learning (DL) algorithms have been prompting a way to take this problem. This thesis has the aim to have an impact in biosignal research and industry by presenting DL solutions that could empower this field. For this purpose an extensive study of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed. Different architecture configurations were explored for signal processing and decision making and were implemented in three different scenarios: (1) Biosignal learning and synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram (ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate autonomously three types of biosignals with a high degree of confidence. As for (2) three CNN-based architectures, and a RNN-based architecture (same used in (1)) were used for both biometric identification, reaching values above 90% for electrode-base datasets (Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal and detection of its deviation was made and tested in two different scenarios: presence of noise using autoencoder and fully-connected network (reaching 99% accuracy for binary classification and 71% for multi-class), and; arrhythmia events by including a RNN to the previous architecture (57% accuracy and 61% sensitivity). In sum, these systems are shown to be capable of producing novel results. The incorporation of several AI systems into one could provide to be the next generation of preventive medicine, as the machines have access to different physiological and anatomical states, it could produce more informed solutions for the issues that one may face in the future increasing the performance of autonomous preventing systems that could be used in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context as explainable AI could trigger this perception and link the inner states of a network with the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda (DL) têm sido usados na procura de uma solução para este problema. Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais, apresentando soluções de DL que poderiam melhorar esta área de investigação. Para esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para o estudo de biosinais. Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança. Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN (a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT -BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN na arquitetura anterior (57 % de precisão e 61 % de sensibilidade). Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir resultados inovadores. A incorporação de vários sistemas de inteligência artificial em um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os estados internos de uma rede às características biológicas

Physiological Self Regulation with Biofeedback Games

Mental stress is a global epidemic that can have serious health consequences including cardiovascular diseases and diabetes. Several techniques are available to teach stress self-regulation skills including therapy, meditation, deep breathing, and biofeedback. While effective, these methods suffer from high drop-outs due to the monotonic nature of the exercises and are generally practiced in quiet relaxed environment, which may not transfer to real-world scenarios. To address these issues, this dissertation presents a novel intervention for stress training using games and wearable sensors. The approach consists of monitoring the user’s physiological signals during gameplay, mapping them into estimates of stress levels, and adapting the game in a way that promotes states of low arousal. This approach offers two key advantages. First, it allows users to focus on the gameplay rather than on monitoring their physiological signals, which makes the training far more engaging. More importantly, it teaches users to self-regulate their stress response, while performing a task designed to increase arousal. Within this broad framework, this dissertation studies three specific problems. First, the dissertation evaluates three physiological signals (breathing rate, heart rate variability, and electrodermal activity) that span across the dimensions of degrees of selectivity in measuring arousal and voluntary control in their effectiveness in lowering arousal. This will identify the signal appropriate for game based stress training and the associated bio-signal processing techniques for real-time arousal estimation. Second, this dissertation investigates different methods of biofeedback presentation e.g. visual feedback and game adaptation during gameplay. Selection of appropriate biofeedback mechanism is critical since it provides the necessary information to improve the perception of visceral states (e.g. stress) to the user. Furthermore, these modalities facilitate skill acquisition in distinct ways (i.e., top-down and bottom-up learning) and influence retention of skills. Third, this dissertation studies reinforcement scheduling in a game and its effect on skill learning and retention. A reinforcement schedule determines which occurrences of the target response are reinforced. This study focuses on continuous and partial reinforcement schedules in GBF and their effect on resistance to extinction (i.e. ability to retain learned skills) after the biofeedback is removed. The main contribution of this dissertation is in demonstrating that stress self-regulation training can be embedded in videogames and help individuals develop more adaptive responses to reduce physiological stress encountered both at home and work

EEG and ECG nonlinear and spectral multiband analysis to explore the effect of videogames against anxiety

Currently, the use of video games has purposes that go beyond entertainment and has been gaining prominence in the health area. In this sense, it was hypothesized that it is possible to discriminate biological signals, namely electrocardiographic and electroencephalographic signals, collected from different participants stimulated through three different commercial video games, Tetris, Bejeweled and Energy. To test this hypothesis, a protocol was developed with the Trier Social Stress Test to induce and dose stress in the subjects to similar levels before each game session, in order to observe the effects of the three test games (3 study groups) at the physiological level. Initially collected at 2000 Hz, the signals were resampled to 500 Hz and filtered using a Butterworth low-pass filter. After filtering the signals, several representative features of the study signals were collected. These features consisted of a series of nonlinear metrics such as the Lyapunov exponent and Correlation Dimension, self-similarity metrics such as the Hurst exponent, and detrended fluctuation analysis, fractal dimensions - such as the Katz and Higuchi fractal dimensions - and metrics of signal chaos and activity, such as signal energy, Logarithmic entropy and Shannon entropy, and a number of spectral metrics for the EEG signal, which should be able to help identify any differences in the stress response. As a final result, a discrimination accuracy of 100% was obtained to discriminate the three study groups, using the top 20% of features selected by the F-score technique, using the coarse K Nearest Neighbor classifier.Atualmente, o uso de videojogos tem propósitos que vão além do entretenimento e tem vindo a ganhar destaque na área da saúde. Nesse sentido, foi formulada a hipótese de que é possível discriminar sinais biológicos, nomeadamente os sinais eletrocardiográficos e eletroencefalográficos, recolhidos de diferentes participantes estimulados através de três videojogos comerciais diferentes, Tetris, Bejeweled e Energy. Para testar esta hipótese foi desenvolvido um protocolo com o Trier Social Stress Test para induzir e dosear o stress nos sujeitos para níveis semelhantes antes de cada sessão de jogo, de forma a observar os efeitos dos três jogos de teste (3 grupos de estudo) a nível fisiológico. Recolhidos inicialmente a 2000 Hz, os sinais foram reamostrados a 500 Hz e filtrados utilizando um filtro passa-baixo de Butterworth. Após filtragem dos sinais, recolheram-se várias características representativas dos sinais de estudo. Estas características consistiram numa série de métricas não lineares, como o expoente de Lyapunov e a Dimensão de Correlação, métricas de auto similaridade como o exponente de Hurst e a análise de flutuação com trends removidas, dimensões fractais - como as dimensões fractais de Katz e Higuchi - e métricas de caos e atividade dos sinais, como a energia dos sinais, a entropia Logarítmica e a entropia de Shannon, e uma série de métricas espectrais para o sinal EEG, que devem ser capazes de ajudar a identificar qualquer diferença na resposta ao stress. Como resultado final obteve-se uma precisão de discriminação de 100% para discriminar os três grupos de estudo, utilizando as 20% das melhores características selecionadas pela técnica de F-score, recorrendo ao classificador coarse K Nearest Neighbor