What Could a Body Tell a Social Robot that It Does Not Know?

Küster, Dennis; Kappas, Arvid

doi:10.5220/0004892503580367

Cited by 3 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, there are reasons to be optimistic when attempting to interpret facial expressions. First, spontaneous consumer responses might be more predictive of affective behavior than abstract and decontextualized situations as typically examined in the laboratory (Küster and Kappas, 2014). Such applied contexts could be more informative about the emotional experience of respondents, thereby increasing the magnitude and coherence of the response.…”

Section: Theoretical Issues: a Lack Of Coherencementioning

confidence: 99%

Opportunities and Challenges for Using Automatic Human Affect Analysis in Consumer Research

et al. 2020

View full text Add to dashboard Cite

The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions, and in naturalistic choice settings. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as a starting point for further analyses. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research.

show abstract

Section: Theoretical Issues: a Lack Of Coherencementioning

confidence: 99%

Opportunities and Challenges for Using Automatic Human Affect Analysis in Consumer Research

et al. 2020

View full text Add to dashboard Cite

show abstract

Do relative positions and proxemics affect the engagement in a Human-Robot collaborative scenario?

Papadopoulos

Küster

Corrigan

et al. 2016

View full text Add to dashboard Cite

This paper investigates the effects of relative position and proxemics in the engagement process involved in Human-Robot collaboration. We evaluate the differences between two experimental placement conditions (frontal vs. lateral) for an autonomous robot in a collaborative task with a user across two different types of robot behaviours (helpful vs. neutral). The study evaluated placement and behaviour types around a touch table with 80 participants by measuring gaze, smiling behaviour, distance from the task, and finally electrodermal activity. Results suggest an overall user preference and higher engagement rates with the helpful robot in the frontal position. We discuss how behaviours and position of the robot relative to a user may affect user engagement and collaboration, in particular when the robot aims to provide help via socio-emotional bonding.

show abstract