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

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Improving Engagement Assessment by Model Individualization and Deep Learning

This dissertation studies methods that improve engagement assessment for pilots. The major work addresses two challenging problems involved in the assessment: individual variation among pilots and the lack of labeled data for training assessment models. Task engagement is usually assessed by analyzing physiological measurements collected from subjects who are performing a task. However, physiological measurements such as Electroencephalography (EEG) vary from subject to subject. An assessment model trained for one subject may not be applicable to other subjects. We proposed a dynamic classifier selection algorithm for model individualization and compared it to other two methods: base line normalization and similarity-based model replacement. Experimental results showed that baseline normalization and dynamic classifier selection can significantly improve cross-subject engagement assessment. For complex tasks such as piloting an air plane, labeling engagement levels for pilots is challenging. Without enough labeled data, it is very difficult for traditional methods to train valid models for effective engagement assessment. This dissertation proposed to utilize deep learning models to address this challenge. Deep learning models are capable of learning valuable feature hierarchies by taking advantage of both labeled and unlabeled data. Our results showed that deep models are better tools for engagement assessment when label information is scarce. To further verify the power of deep learning techniques for scarce labeled data, we applied the deep learning algorithm to another small size data set, the ADNI data set. The ADNI data set is a public data set containing MRI and PET scans of Alzheimer\u27s Disease (AD) patients for AD diagnosis. We developed a robust deep learning system incorporating dropout and stability selection techniques to identify the different progression stages of AD patients. The experimental results showed that deep learning is very effective in AD diagnosis. In addition, we studied several imbalance learning techniques that are useful when data is highly unbalanced, i.e., when majority classes have many more training samples than minority classes. Conventional machine learning techniques usually tend to classify all data samples into majority classes and to perform poorly for minority classes. Unbalanced learning techniques can balance data sets before training and can improve learning performance

Assessing the Human Factor in Truck Driving

Human factors assessment techniques are commonly applied to a variety of workplaces to examine the nature of operations and how key functions are controlled operationally; however, these tools appear to overlook key aspects of truck driving, particularly the driver’s relationship to the driving experience. The fundamental issue is with the ability to completely decompose truck driving and accurately document the truck drivers working environment will be problematic. Therefore, to demonstrate how a truck driver moves between each series of sub-tasks will require a purpose-built assessment tool that that is both practical and relevant to truck driving

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others