Abstract
This book aims to investigate the features that are at the core of human interactions to model the involved emotional processes, in order to design and develop autonomous systems and algorithms able to detect early signs of changes, in moods and emotional states. The attention is focused on emotional social features and the human’s ability to decode and encode emotional social cues while interacting. In order to do this, the book will propose a series of investigations that gather behavioral data from speech, handwriting, facial, vocal and gestural expressions. This is done through the definition of behavioral tasks that may serve to produce changes in the perception of emotional social cues. Specific scenarios are designed to assess users’ emphatic and social competencies. The collected data are used to gain knowledge on how behavioral and interactional features are affected by individuals’ moods and emotional states. This information can be exploited to devise multidimensional models of multimodal interactional features that will serve for measuring the degree of empathic relationships developed between individuals and allow the design and development of cost-effective emotion-aware technologies to be used in applicative contexts such as remote health care services and robotic assistance.
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Esposito, A., Jain, L.C. (2016). Modeling Emotions in Robotic Socially Believable Behaving Systems. In: Esposito, A., Jain, L. (eds) Toward Robotic Socially Believable Behaving Systems - Volume I . Intelligent Systems Reference Library, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-31056-5_2
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