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Gesture Spotting and Recognition for Human-Robot Interaction.

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Title: Gesture Spotting and Recognition for Human-Robot Interaction.
Authors: Hee-Deok Yang1 hdyang@image.korea.ac.kr, A-yeon Park1, Seong-Whan Lee1 swlee@image.korea.ac.kr
Source: IEEE Transactions on Robotics. Apr2007, Vol. 23 Issue 2, p256-270. 15p. 23 Diagrams.
Subjects: Mobile robots, Human-machine relationship, Hidden Markov models, Robotics, Robot kinematics
Abstract: Visual interpretation of gestures can be useful in accomplishing natural human-robot interaction (HRI). Previous HRI research focused on issues such as hand gestures, sign language, and command gesture recognition. Automatic recognition of whole-body gestures is required in order for HRI to operate naturally. This presents a challenging problem, because describing and modeling meaningful gesture patterns from whole-body gestures is a complex task. This paper presents a new method for recognition of whole-body key gestures in HRI. A human subject is first described by a set of features, encoding the angular relationship between a dozen body parts in 3-D. A feature vector is then mapped to a codeword of hidden Markov models. In order to spot key gestures accurately, a sophisticated method of designing a transition gesture model is proposed. To reduce the states of the transition gesture model, model reduction which merges similar states based on data-dependent statistics and relative entropy is used. The experimental results demonstrate that the proposed method can be efficient and effective in HRI, for automatic recognition of whole-body key gestures from motion sequences. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Robotics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Gesture Spotting and Recognition for Human-Robot Interaction.
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  Data: <searchLink fieldCode="AR" term="%22Hee-Deok+Yang%22">Hee-Deok Yang</searchLink><relatesTo>1</relatesTo><i> hdyang@image.korea.ac.kr</i><br /><searchLink fieldCode="AR" term="%22A-yeon+Park%22">A-yeon Park</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Seong-Whan+Lee%22">Seong-Whan Lee</searchLink><relatesTo>1</relatesTo><i> swlee@image.korea.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Robotics%22">IEEE Transactions on Robotics</searchLink>. Apr2007, Vol. 23 Issue 2, p256-270. 15p. 23 Diagrams.
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  Data: <searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink><br /><searchLink fieldCode="DE" term="%22Human-machine+relationship%22">Human-machine relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br /><searchLink fieldCode="DE" term="%22Robotics%22">Robotics</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+kinematics%22">Robot kinematics</searchLink>
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  Data: Visual interpretation of gestures can be useful in accomplishing natural human-robot interaction (HRI). Previous HRI research focused on issues such as hand gestures, sign language, and command gesture recognition. Automatic recognition of whole-body gestures is required in order for HRI to operate naturally. This presents a challenging problem, because describing and modeling meaningful gesture patterns from whole-body gestures is a complex task. This paper presents a new method for recognition of whole-body key gestures in HRI. A human subject is first described by a set of features, encoding the angular relationship between a dozen body parts in 3-D. A feature vector is then mapped to a codeword of hidden Markov models. In order to spot key gestures accurately, a sophisticated method of designing a transition gesture model is proposed. To reduce the states of the transition gesture model, model reduction which merges similar states based on data-dependent statistics and relative entropy is used. The experimental results demonstrate that the proposed method can be efficient and effective in HRI, for automatic recognition of whole-body key gestures from motion sequences. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of IEEE Transactions on Robotics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TRO.2006.889491
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        Type: general
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      – SubjectFull: Hidden Markov models
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      – SubjectFull: Robot kinematics
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              Text: Apr2007
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