NDLI: Context-based conversational hand gesture classification in narrative interaction

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Context-based conversational hand gesture classification in narrative interaction

Content Provider	ACM Digital Library
Author	Sumi, Yasuyuki Nitta, Katsumi Okada, Shogo Bono, Mayumi Takanashi, Katsuya
Abstract	Communicative hand gestures play important roles in face-to-face conversations. These gestures are arbitrarily used depending on an individual; even when two speakers narrate the same story, they do not always use the same hand gesture (movement, position, and motion trajectory) to describe the same scene. In this paper, we propose a framework for the classification of communicative gestures in small group interactions. We focus on how many times the hands are held in a gesture and how long a speaker continues a hand stroke, instead of observing hand positions and hand motion trajectories. In addition, to model communicative gesture patterns, we use nonverbal features of participants addressed from participant gestures. In this research, we extract features of gesture phases defined by Kendon (2004) and co-occurring nonverbal patterns with gestures, i.e., utterance, head gesture, and head direction of each participant, by using pattern recognition techniques. In the experiments, we collect eight group narrative interaction datasets to evaluate the classification performance. The experimental results show that gesture phase features and nonverbal features of other participants improves the performance to discriminate communicative gestures that are used in narrative speeches and other gestures from 4% to 16%.
Starting Page	303
Ending Page	310
Page Count	8
File Format	PDF
ISBN	9781450321297
DOI	10.1145/2522848.2522898
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-12-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Small group Conversation analysis Contextual information Hand gesture recognition
Content Type	Text
Resource Type	Article

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