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2017 | OriginalPaper | Buchkapitel

Boredom Recognition Based on Users’ Spontaneous Behaviors in Multiparty Human-Robot Interactions

verfasst von : Yasuhiro Shibasaki, Kotaro Funakoshi, Koichi Shinoda

Erschienen in: MultiMedia Modeling

Verlag: Springer International Publishing

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Abstract

To recognize boredom in users interacting with machines is valuable to improve user experiences in human-machine long term interactions, especially for intelligent tutoring systems, health-care systems, and social assistants. This paper proposes a two-staged framework and feature design for boredom recognition in multiparty human-robot interactions. At the first stage the proposed framework detects boredom-indicating user behaviors based on skeletal data obtained by motion capture, and then it recognizes boredom in combination with detection results and two types of multiparty information, i.e., gaze direction to other participants and incoming-and-outgoing of participants. We experimentally confirmed the effectiveness of both the proposed framework and the multiparty information. In comparison with a simple baseline method, the proposed framework gained 35% points in the F1 score.

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Vorheriges Kapitel i-Stylist: Finding the Right Dress Through Your Social Networks
Nächstes Kapitel Classification of sMRI for AD Diagnosis with Convolutional Neuronal Networks: A Pilot 2-D+ Study on ADNI
Fußnoten
1
This is intended to make an annotator’s judgement on an interval independent of the neighboring intervals as much as possible in a reasonable cost of annotation. In forward annotation, the influence of the judgement of an interval on the judgement of the next interval could be larger. The playback of each interval was of course forwarded.
 
2
Although it is known that there are several types of boredom [6], we employ a naive notion of boredom for the sake of simplicity.
 
3
The meanings of strong/weak are different between the boredom state and the other boredom behavior categories (BB-*). For the boredom state, ‘strong’, ‘weak’, and ‘none’ respectively mean the samples with boredom state labels ‘BORED’, ‘MAYBE’, and ‘NOT BORED’. For the other behavior categories, ‘strong’ means the samples that contain relevant behaviors more than 2.5 s. ‘weak’ means those less than 2.5 s, and ‘none’ means those with no relevant behaviors.
 
4
‘#targets’ means the numbers of samples in column ‘strong’ shown in Table 2. ‘#residuals’ means the sums of the numbers of the ‘weak’ and ‘none’ samples.
 
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Metadaten
Titel
Boredom Recognition Based on Users’ Spontaneous Behaviors in Multiparty Human-Robot Interactions
verfasst von
Yasuhiro Shibasaki
Kotaro Funakoshi
Koichi Shinoda
Copyright-Jahr
2017
Buch
MultiMedia Modeling

Print ISBN: 978-3-319-51810-7

Electronic ISBN: 978-3-319-51811-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-51811-4_55

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