Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

A Framework for Students Profile Detection

Some of the biggest problems tackling Higher Education Institutions are students’ drop-out and academic disengagement. Physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems, are some of the factors that can lead to it. This problematic is worsened by the shortage of educational resources, that can bridge the communication gap between the faculty staff and the affective needs of these students. This dissertation focus in the development of a framework, capable of collecting analytic data, from an array of emotions, affects and behaviours, acquired either by human observations, like a teacher in a classroom or a psychologist, or by electronic sensors and automatic analysis software, such as eye tracking devices, emotion detection through facial expression recognition software, automatic gait and posture detection, and others. The framework establishes the guidance to compile the gathered data in an ontology, to enable the extraction of patterns outliers via machine learning, which assist the profiling of students in critical situations, like disengagement, attention deficit, drop-out, and other sociological issues. Consequently, it is possible to set real-time alerts when these profiles conditions are detected, so that appropriate experts could verify the situation and employ effective procedures. The goal is that, by providing insightful real-time cognitive data and facilitating the profiling of the students’ problems, a faster personalized response to help the student is enabled, allowing academic performance improvements

Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues

We examine the utility of implicit behavioral cues in the form of EEG brain signals and eye movements for gender recognition (GR) and emotion recognition (ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded (no mask) as well as partially occluded (eye and mouth masked) emotive faces. Obtained experimental results reveal that (a) reliable GR and ER is achievable with EEG and eye features, (b) differential cognitive processing especially for negative emotions is observed for males and females and (c) some of these cognitive differences manifest under partial face occlusion, as typified by the eye and mouth mask conditions.Comment: To be published in the Proceedings of Seventh International Conference on Affective Computing and Intelligent Interaction.201

Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools

While detecting and interpreting temporal patterns of non–verbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human–centered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement

Human-computer interaction in ubiquitous computing environments

Purpose &ndash; The purpose of this paper is to explore characteristics of human-computer interaction when the human body and its movements become input for interaction and interface control in pervasive computing settings. Design/methodology/approach &ndash; The paper quantifies the performance of human movement based on Fitt\u27s Law and discusses some of the human factors and technical considerations that arise in trying to use human body movements as an input medium. Findings &ndash; The paper finds that new interaction technologies utilising human movements may provide more flexible, naturalistic interfaces and support the ubiquitous or pervasive computing paradigm. Practical implications &ndash; In pervasive computing environments the challenge is to create intuitive and user-friendly interfaces. Application domains that may utilize human body movements as input are surveyed here and the paper addresses issues such as culture, privacy, security and ethics raised by movement of a user\u27s body-based interaction styles. Originality/value &ndash; The paper describes the utilization of human body movements as input for interaction and interface control in pervasive computing settings. <br /