The MEI Robot: Towards Using Motherese to Develop Multimodal Emotional Intelligence

Lim, Angelica; Okuno, Hiroshi G.

doi:10.1109/tamd.2014.2317513

Cited by 36 publications

(27 citation statements)

References 54 publications

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“…Several of the affect-detection systems designed for social robots have used dimensional models, mainly consisting of valence and arousal scales for recognition from facial expressions [76,77,125], body language [5], voice [81,82], physiological signals [4,70,86,193], and multi-modal inputs [193,203]. A small number of systems have utilized alternative affect classification scales, such as accessibility [79], engagement [61], predictability [81], stance [90], speed regularity and extent [203], stress [19,85], anxiety [84,190], and aversion and affinity [213].…”

Section: Discussionmentioning

confidence: 99%

“…Sensors include ECG and EMG [73,83] to record physiological signals, microphones [83,90,199,200,202,203,205] to record voice intonations, and 2D cameras to capture facial information [90,199,200,202,205], and body language information [203]. These sensors have been integrated together in order to extract many features, including heart rate [73,83], voice pitch [83,90,199,200,202,203,205], gait features [203] and facial features [90,199,200,202,205]. Future research should continue to investigate a wide range of features for all modes in order to determine which combinations of features result in the highest recognition rates during real-world interactions.…”

Section: Discussionmentioning

confidence: 99%

“…Future research on systems for detecting affect from physiological signals should also focus on providing more quantitative results with respect to the recognition of specific affective states/levels as is the standard with the other modes. Classification techniques utilized by multi-modal systems included HMMs [83], Bayesian network [73,199], SVMs [200,202], NNs [90], and statistical methods [203].…”

Section: Discussionmentioning

confidence: 99%

“…In [203], the humanoid robot, Nao, was used to simulate a child for adult-child interaction. Interactions consisted of the adult greeting the robot, showing a toy to the child robot, revoking the toy, and saying goodbye.…”

Section: Multi-purpose Hrimentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

McColl

Hong

Hatakeyama

et al. 2015

J Intell Robot Syst

104

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In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world humancentered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Multi-purpose Hrimentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

McColl

Hong

Hatakeyama

et al. 2015

J Intell Robot Syst

104

View full text Add to dashboard Cite

show abstract

“…However, many challenges need to be addressed in order to meet such a requirement (Baker et al, 2009a;Moore, 2013Moore, , 2015, not least how to evolve the complexity of voice-based interfaces from simple structured dialogs to more flexible conversational designs without confusing the user (Bernsen et al, 1998;McTear, 2004;Lopez Cozar Delgado and Araki, 2005;Phillips and Philips, 2006;Moore, 2016b). In particular, seminal work by Nass and Brave (2005) showed how attention needs to be paid to users' expectations [e.g., selecting the "gender" of a system's voice (Crowell et al, 2009)], and this has inspired work on "empathic" vocal robots (Breazeal, 2003;Fellous and Arbib, 2005;Haring et al, 2011;Eyssel et al, 2012;Lim and Okuno, 2014;Crumpton and Bethel, 2016). On the other hand, user interface experts, such as Balentine (2007), have argued that such agents should be clearly machines rather than emulations of human beings, particularly to avoid the "uncanny valley effect" (Mori, 1970), whereby mismatched perceptual cues can lead to feelings of repulsion (Moore, 2012).…”

Section: Spoken Language Systemsmentioning

confidence: 99%

Vocal Interactivity in-and-between Humans, Animals, and Robots

2016

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Almost all animals exploit vocal signals for a range of ecologically motivated purposes: detecting predators/prey and marking territory, expressing emotions, establishing social relations, and sharing information. Whether it is a bird raising an alarm, a whale calling to potential partners, a dog responding to human commands, a parent reading a story with a child, or a business-person accessing stock prices using Siri, vocalization provides a valuable communication channel through which behavior may be coordinated and controlled, and information may be distributed and acquired. Indeed, the ubiquity of vocal interaction has led to research across an extremely diverse array of fields, from assessing animal welfare, to understanding the precursors of human language, to developing voice-based human-machine interaction. Opportunities for cross-fertilization between these fields abound; for example, using artificial cognitive agents to investigate contemporary theories of language grounding, using machine learning to analyze different habitats or adding vocal expressivity to the next generation of language-enabled autonomous social agents. However, much of the research is conducted within well-defined disciplinary boundaries, and many fundamental issues remain. This paper attempts to redress the balance by presenting a comparative review of vocal interaction within-and-between humans, animals, and artificial agents (such as robots), and it identifies a rich set of open research questions that may benefit from an interdisciplinary analysis.

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