文章基本信息

标题：User Affect Elicitation with a Socially Emotional Robot
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作者：Mingyang Shao ; Matt Snyder ; Goldie Nejat 等
期刊名称：Robotics
电子版ISSN：2218-6581
出版年度：2020
卷号：9
期号：2
页码：44-60
DOI：10.3390/robotics9020044
出版社：MDPI Publishing
摘要：To effectively communicate with people, social robots must be capable of detecting, interpreting, and responding to human affect during human–robot interactions (HRIs). In order to accurately detect user affect during HRIs, affect elicitation techniques need to be developed to create and train appropriate affect detection models. In this paper, we present such a novel affect elicitation and detection method for social robots in HRIs. Non-verbal emotional behaviors of the social robot were designed to elicit user affect, which was directly measured through electroencephalography (EEG) signals. HRI experiments with both younger and older adults were conducted to evaluate our affect elicitation technique and compare the two types of affect detection models we developed and trained utilizing multilayer perceptron neural networks (NNs) and support vector machines (SVMs). The results showed that; on average, the self-reported valence and arousal were consistent with the intended elicited affect. Furthermore, it was also noted that the EEG data obtained could be used to train affect detection models with the NN models achieving higher classification rates.
关键词：human–robot interaction; affective computing; assistive and social robotics human–robot interaction ; affective computing ; assistive and social robotics