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      Electrical Impedance Tomography for Artificial Sensitive Robotic Skin: A Review

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          A survey of affect recognition methods: audio, visual, and spontaneous expressions.

          Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.
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            Tactile Sensing—From Humans to Humanoids

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              Human-Robot Interaction: A Survey

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                Author and article information

                Journal
                IEEE Sensors Journal
                IEEE Sensors J.
                Institute of Electrical and Electronics Engineers (IEEE)
                1530-437X
                1558-1748
                April 2015
                April 2015
                : 15
                : 4
                : 2001-2016
                Article
                10.1109/JSEN.2014.2375346
                9e760898-803a-4981-8b56-ab4cedbfea63
                © 2015
                History

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