Automatic detection of service initiation signals used in bars

Loth S, Huth K, de Ruiter J (2013)
Frontiers in Psychology 4: 557.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Recognizing the intention of others is important in all social interactions, especially in the service domain. Enabling a bartending robot to serve customers is particularly challenging as the system has to recognize the social signals produced by customers and respond appropriately. Detecting whether a customer would like to order is essential for the service encounter to succeed. This detection is particularly challenging in a noisy environment with multiple customers. Thus, a bartending robot has to be able to distinguish between customers intending to order, chatting with friends or just passing by. In order to study which signals customers use to initiate a service interaction in a bar, we recorded real-life customer-staff interactions in several German bars. These recordings were used to generate initial hypotheses about the signals customers produce when bidding for the attention of bar staff. Two experiments using snapshots and short video sequences then tested the validity of these hypothesized candidate signals. The results revealed that bar staff responded to a set of two non-verbal signals: first, customers position themselves directly at the bar counter and, secondly, they look at a member of staff. Both signals were necessary and, when occurring together, sufficient. The participants also showed a strong agreement about when these cues occurred in the videos. Finally, a signal detection analysis revealed that ignoring a potential order is deemed worse than erroneously inviting customers to order. We conclude that (a) these two easily recognizable actions are sufficient for recognizing the intention of customers to initiate a service interaction, but other actions such as gestures and speech were not necessary, and (b) the use of reaction time experiments using natural materials is feasible and provides ecologically valid results.
Stichworte
human robot interaction; social signal processing; intention recognition; social robotics; social signaling; action recognition
Erscheinungsjahr
2013
Zeitschriftentitel
Frontiers in Psychology
Band
4
Art.-Nr.
557
ISSN
1664-1078
eISSN
1664-1078
Page URI
https://pub.uni-bielefeld.de/record/2623872

Zitieren

Loth S, Huth K, de Ruiter J. Automatic detection of service initiation signals used in bars. Frontiers in Psychology. 2013;4: 557.
Loth, S., Huth, K., & de Ruiter, J. (2013). Automatic detection of service initiation signals used in bars. Frontiers in Psychology, 4, 557. https://doi.org/10.3389/fpsyg.2013.00557
Loth, Sebastian, Huth, Kerstin, and de Ruiter, Jan. 2013. “Automatic detection of service initiation signals used in bars”. Frontiers in Psychology 4: 557.
Loth, S., Huth, K., and de Ruiter, J. (2013). Automatic detection of service initiation signals used in bars. Frontiers in Psychology 4:557.
Loth, S., Huth, K., & de Ruiter, J., 2013. Automatic detection of service initiation signals used in bars. Frontiers in Psychology, 4: 557.
S. Loth, K. Huth, and J. de Ruiter, “Automatic detection of service initiation signals used in bars”, Frontiers in Psychology, vol. 4, 2013, : 557.
Loth, S., Huth, K., de Ruiter, J.: Automatic detection of service initiation signals used in bars. Frontiers in Psychology. 4, : 557 (2013).
Loth, Sebastian, Huth, Kerstin, and de Ruiter, Jan. “Automatic detection of service initiation signals used in bars”. Frontiers in Psychology 4 (2013): 557.
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5 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Confidence in uncertainty: Error cost and commitment in early speech hypotheses.
Loth S, Jettka K, Giuliani M, Kopp S, de Ruiter JP., PLoS One 13(8), 2018
PMID: 30067853
Systematic analysis of video data from different human-robot interaction studies: a categorization of social signals during error situations.
Giuliani M, Mirnig N, Stollnberger G, Stadler S, Buchner R, Tscheligi M., Front Psychol 6(), 2015
PMID: 26217266
Ghost-in-the-Machine reveals human social signals for human-robot interaction.
Loth S, Jettka K, Giuliani M, de Ruiter JP., Front Psychol 6(), 2015
PMID: 26582998

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