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      Predicting users’ task difficulty using Social Signals: a Preliminary Model

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      The 26th BCS Conference on Human Computer Interaction (HCI)

      Human Computer Interaction

      12 - 14 September 2012

      Nonverbal Behaviour, Social Signals, Thin Slices, Video Coding, Video Processing

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          Abstract

          Humans communicate social intentions through patterns of nonverbal language, using posture, gestures and body motion. This social signalling is present in human to human interaction as well as in human-computer interaction. Our daily dependence on computers emphasizes the need and importance for good interaction quality. While humans have an innate ability to recognize and respond to social signalling, machines don’t. Our work aims to develop a Social Signal Processing model based on features extracted using simple video processing techniques, applied in a real context and running in real-time, to predict interaction’s difficulties and problems. In this study we report a preliminary model where features extracted from user motion within 60 seconds of video recordings can predict 46,6% of variance in task difficulty.

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          Most cited references 4

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          Thin slices of negotiation: predicting outcomes from conversational dynamics within the first 5 minutes.

          In this research the authors examined whether conversational dynamics occurring within the first 5 minutes of a negotiation can predict negotiated outcomes. In a simulated employment negotiation, microcoding conducted by a computer showed that activity level, conversational engagement, prosodic emphasis, and vocal mirroring predicted 30% of the variance in individual outcomes. The conversational dynamics associated with success among high-status parties were different from those associated with success among low-status parties. Results are interpreted in light of theory and research exploring the predictive power of "thin slices" of behavior (N. Ambady & R. Rosenthal, 1992). Implications include the development of new technology to diagnose and improve negotiation processes.
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            Multimodal affect recognition in learning environments

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              Automatic analysis of affective postures and body motion to detect engagement with a game companion

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                Author and article information

                Contributors
                Conference
                September 2012
                September 2012
                : 1-4
                Affiliations
                engageLab

                University of Minho,

                Portugal
                engageLab / Dep. Com.

                Sciences

                University of Minho,

                Portugal
                School of Technology

                and Management of

                Felgueiras / engageLab

                University of Minho,

                Portugal
                engageLab / Dep.

                Industrial Elec.

                University of Minho,

                Portugal
                Center for Learn. and

                Know. Tech.

                Linnaeus University,

                Sweden; and University

                of Minho, Portugal
                engageLab / Dep. Inf.

                Systems

                University of Minho,

                Portugal
                Article
                10.14236/ewic/HCI2012.109
                © João Pedro Ferreira et al. Published by BCS Learning and Development Ltd. The 26th BCS Conference on Human Computer Interaction, Birmingham, UK

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                The 26th BCS Conference on Human Computer Interaction
                HCI
                26
                Birmingham, UK
                12 - 14 September 2012
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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