Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

abstract: Parents fulfill a pivotal role in early childhood development of social and communication skills. In children with autism, the development of these skills can be delayed. Applied behavioral analysis (ABA) techniques have been created to aid in skill acquisition. Among these, pivotal response treatment (PRT) has been empirically shown to foster improvements. Research into PRT implementation has also shown that parents can be trained to be effective interventionists for their children. The current difficulty in PRT training is how to disseminate training to parents who need it, and how to support and motivate practitioners after training. Evaluation of the parents’ fidelity to implementation is often undertaken using video probes that depict the dyadic interaction occurring between the parent and the child during PRT sessions. These videos are time consuming for clinicians to process, and often result in only minimal feedback for the parents. Current trends in technology could be utilized to alleviate the manual cost of extracting data from the videos, affording greater opportunities for providing clinician created feedback as well as automated assessments. The naturalistic context of the video probes along with the dependence on ubiquitous recording devices creates a difficult scenario for classification tasks. The domain of the PRT video probes can be expected to have high levels of both aleatory and epistemic uncertainty. Addressing these challenges requires examination of the multimodal data along with implementation and evaluation of classification algorithms. This is explored through the use of a new dataset of PRT videos. The relationship between the parent and the clinician is important. The clinician can provide support and help build self-efficacy in addition to providing knowledge and modeling of treatment procedures. Facilitating this relationship along with automated feedback not only provides the opportunity to present expert feedback to the parent, but also allows the clinician to aid in personalizing the classification models. By utilizing a human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the classification models by providing additional labeled samples. This will allow the system to improve classification and provides a person-centered approach to extracting multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Towards personalized medicine in psychosis: the roles of social cognition and metacognition

[eng] People with psychosis experience a range of symptoms and impairments that significantly impact their lives and often concur with disability. The best predictors of functional outcome are social cognition and metacognition, which are often impaired in psychosis. Interventions to improve both domains are effective, but this efficacy does not always translate into better functioning. Delivering early, and targeted social cognitive or metacognitive interventions to patients with psychosis could be instrumental in preventing poor functional outcome and preventing relapse, but the grounds on how to personalize these interventions are unknown. Although it has been suggested that the approach should take sex differences, the refining of measurement instruments and the use of sophisticated statistical models, these have not been explored yet. Under this rationale, the present doctoral dissertation aims to: 1) validate a test of facial emotion recognition (Baron Cohen’s Face Test) in healthy population, with the aim of detecting whether it is an appropriate tool to use in clinical research, 2) detect whether patients with first episode psychosis have different, clinically meaningful profiles of performance in social cognition and metacognition, 3) explore the sociodemographic, clinical, and neurocognitive characteristics of each profile, 4) examine if males and females with first episode psychosis are similar in their heterogeneity in social cognition and metacognition, 5) explore the role of social cognition and sex in functional outcome in people with established psychosis (schizophrenia). This broad aim yielded four research articles. The main findings of this doctoral dissertation are a) Baron Cohen’s Face Test is an adequate and reliable instrument to measure facial emotion recognition in Spanish population but it presents a ceiling effect, b) People with first-episode psychosis have distinct profiles of social cognition and metacognition that have specific clinical and neurocognitive correlates. Having worse social cognition is associated with worse clinical presentation, even if metacognition is preserved, c) Men and women with first-episode psychosis have similar configurations of social cognition and metacognition. However, there are sex- specific profiles that should be considered when delivering treatment. Sex-specific profiles seem to be associated with more severity of the disorder than the common profiles. These results suggest that people with psychosis can receive social cognitive or metacognitive targeted treatment as early as after the first episode, but these should be chosen considering the profile of each individual and their biological sex. Thus, patients with psychosis should always be carefully assessed for social cognitive and metacognitive performance.[spa] Las personas con psicosis experimentan una serie de síntomas y déficits que afectan significativamente a sus vidas y que a menudo concurren con la discapacidad. Los mejores predictores de funcionamiento son la cognición social y la metacognición, que a menudo presentan deterioro en personas con psicosis. Diversas intervenciones para mejorar ambos dominios son eficaces, pero esto no siempre se traduce en un mejor funcionamiento. Para ello, se ha propuesto que intervenciones en cognición social y metacognición tempranas y dirigidas podrían maximizar su efecto sobre el funcionamiento y la prevención de recaídas. No obstante, se desconocen los fundamentos que debería guiar su personalización. Aunque se ha sugerido que el enfoque debería tener en cuenta las diferencias de sexo, el perfeccionamiento de los instrumentos de medida y el uso de modelos estadísticos sofisticados, éstos aún no se han explorado en la literatura. Bajo este razonamiento, la presente tesis doctoral pretende: 1) validar una prueba de reconocimiento facial de emociones (Test de Caras de Baron Cohen) en población sana, con el objetivo de detectar si es un instrumento adecuado para utilizar en la investigación clínica, 2) detectar si los pacientes con primer episodio de psicosis tienen perfiles diferentes y clínicamente significativos de rendimiento en cognición social y metacognición, 3) explorar las características sociodemográficas, clínicas y neurocognitivas de cada perfil, 4) examinar si los hombres y las mujeres con primer episodio psicótico son similares en su heterogeneidad en la cognición social y la metacognición, 5) explorar el papel de la cognición social y el sexo en el resultado funcional en personas con psicosis establecida (esquizofrenia). Este amplio objetivo dio lugar a cuatro artículos de investigación. Los principales hallazgos de esta tesis doctoral son: a) El Test de Caras de Baron Cohen es un instrumento adecuado y fiable para medir el reconocimiento de emociones faciales en población española, pero presenta un efecto techo, b) Las personas con primer episodio psicótico tienen perfiles distintos de cognición social y metacognición, con correlatos clínicos y neurocognitivos específicos asociados. Tener una peor cognición social se asocia con una peor presentación clínica, incluso si la metacognición está preservada, c) Los hombres y las mujeres con primer episodio psicótico tienen configuraciones similares de cognición social y metacognición. Sin embargo, existen perfiles específicos de cada sexo que deben tenerse en cuenta a la hora de aplicar el tratamiento, ya que éstos parecen estar asociados a una mayor gravedad del trastorno que los perfiles comunes. Estos resultados sugieren que las personas con psicosis pueden recibir tratamiento en cognición social o metacognición específico desde el primer episodio psicótico, pero éste debe elegirse teniendo en cuenta el perfil de cada individuo y su sexo biológico. Para ello, se pone de manifiesto la necesidad de una correcta evaluación de sus habilidades cognitivo-sociales y metacognitivas

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area