Prediction of Affective States through Non-invasive Thermal Camera and EMG Recordings

Gökçay, Didem; Baltaci, Serdar; Karahan, Cagri; Turkay, Kemal Dogus

doi:10.1007/978-3-642-24571-8_41

Cited by 1 publication

(1 citation statement)

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“…One system has been proposed for classifying emotional states based on the dimensional model of valence and arousal. This system consists of a wireless EEG cap from EMOTIV that quantifies EMG signals as a measure of valence, and thermal cameras that predict arousal by detecting changes in blood flow in the face 58 . Another alternative to optical approaches is a wearable interface has been developed to analyze bioelectrical data from electrodes positioned on the side of the face that detect signals distally and continuously while being unobtrusive to the user.…”

Section: Face Recognition Systemsmentioning

confidence: 99%

An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

et al. 2014

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Understanding intentions is a complex social-cognitive task for humans, let alone machines. In this paper we discuss how the developing field of Social Signal Processing, and assessing social cues to interpret social signals, may help to develop a foundation for robotic social intelligence. We describe a taxonomy to further R&D in HRI and facilitate natural interactions between humans and robots. This is based upon an interdisciplinary framework developed to integrate: (1) the sensors used for detecting social cues, (2) the parameters for differentiating and classifying differing levels of those cues, and (3) how sets of social cues indicate specific social signals. This is necessarily an iterative process, as technologies improve and social science researchers better understand the complex interactions of vast quantities of social cue combinations. As such, the goal of this paper is to advance a taxonomy of this nature to further stimulate interdisciplinary collaboration in the development of advanced social intelligence that mutually informs areas of robotic perception and intelligence.

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Section: Face Recognition Systemsmentioning

confidence: 99%