Applying Pattern-based Classification to Sequences of Gestures

Suzanne Aussems; Mingyuan Chu; Sotaro Kita; Menno van Zaanen

Applying Pattern-based Classification to Sequences of Gestures

Suzanne Aussems, Mingyuan Chu, Sotaro Kita, Menno van Zaanen

Psychology

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

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Abstract

The pattern-based sequence classification system (PBSC) identifies regularly occurring patterns in sequential data and uses these patterns to predict meta-information. To illustrate the wide applicability of this approach, we classify speech-accompanying gestures produced by adults in order to predict their level of empathy. Previous research that focused on isolated gestures has shown that the frequency with which individuals produce certain speech-accompanying gestures is related to empathy. The current research extends these analyses by investigating the relationship between multi-gesture sequences and empathy. Patterns found in multi-gesture sequences prove to be more useful for predicting empathy levels in adults than patterns found in single gestures. This paper thus demonstrates that sequences of gestures contain additional information compared to gestures in isolation. More importantly, this study introduces PBSC as an effective method to incorporate time as an extra dimension in gestural communication, which can be extended to a wide range of sequential modalities.

Original language	English
Title of host publication	Proceedings of the 37th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Publisher	Cogsci
Pages	124-129
Number of pages	6
ISBN (Print)	978-0-9911967-2-2
Publication status	Published - 2015
Event	37th Annual Meeting of the Cognitive Science Society - Pasadena, California, United States Duration: 22 Jul 2015 → 22 Jul 2015

Conference

Conference	37th Annual Meeting of the Cognitive Science Society
Country/Territory	United States
City	California
Period	22/07/15 → 22/07/15

Bibliographical note

This research was supported by an Economic and Social Research Council Grant RES-062-23-2002 granted to Sotaro Kita and Antje Meyer. We thank Antje Meyer for allowing the use of the dataset. Our gratitude goes to Farzana Bhaiyat, Christina Chelioti, Dayal Dhiman, Lucy Foulkes, Rachel Furness, Alicia Griffiths, Beatrice Hannah, Sagar Jilka, Johnny King Lau, Valentina Lee, Zeshu Shao, Callie Steadman, and Laura Torney for their help with data collection and coding. We thank Birmingham City University, Bishop Vesey’s Grammar School, City College Birmingham, CTC Kingshurst Academy, and University College Birmingham
for their participation in our research.

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Aussems Chu Kita & Zaanen 2015 CogsciAccepted author manuscript, 116 KBLicence: Unspecified

Cite this

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title = "Applying Pattern-based Classification to Sequences of Gestures",

abstract = "The pattern-based sequence classification system (PBSC) identifies regularly occurring patterns in sequential data and uses these patterns to predict meta-information. To illustrate the wide applicability of this approach, we classify speech-accompanying gestures produced by adults in order to predict their level of empathy. Previous research that focused on isolated gestures has shown that the frequency with which individuals produce certain speech-accompanying gestures is related to empathy. The current research extends these analyses by investigating the relationship between multi-gesture sequences and empathy. Patterns found in multi-gesture sequences prove to be more useful for predicting empathy levels in adults than patterns found in single gestures. This paper thus demonstrates that sequences of gestures contain additional information compared to gestures in isolation. More importantly, this study introduces PBSC as an effective method to incorporate time as an extra dimension in gestural communication, which can be extended to a wide range of sequential modalities.",

author = "Suzanne Aussems and Mingyuan Chu and Sotaro Kita and {van Zaanen}, Menno",

note = "This research was supported by an Economic and Social Research Council Grant RES-062-23-2002 granted to Sotaro Kita and Antje Meyer. We thank Antje Meyer for allowing the use of the dataset. Our gratitude goes to Farzana Bhaiyat, Christina Chelioti, Dayal Dhiman, Lucy Foulkes, Rachel Furness, Alicia Griffiths, Beatrice Hannah, Sagar Jilka, Johnny King Lau, Valentina Lee, Zeshu Shao, Callie Steadman, and Laura Torney for their help with data collection and coding. We thank Birmingham City University, Bishop Vesey{\textquoteright}s Grammar School, City College Birmingham, CTC Kingshurst Academy, and University College Birmingham for their participation in our research. ; 37th Annual Meeting of the Cognitive Science Society ; Conference date: 22-07-2015 Through 22-07-2015",

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AU - Chu, Mingyuan

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AU - van Zaanen, Menno

N1 - This research was supported by an Economic and Social Research Council Grant RES-062-23-2002 granted to Sotaro Kita and Antje Meyer. We thank Antje Meyer for allowing the use of the dataset. Our gratitude goes to Farzana Bhaiyat, Christina Chelioti, Dayal Dhiman, Lucy Foulkes, Rachel Furness, Alicia Griffiths, Beatrice Hannah, Sagar Jilka, Johnny King Lau, Valentina Lee, Zeshu Shao, Callie Steadman, and Laura Torney for their help with data collection and coding. We thank Birmingham City University, Bishop Vesey’s Grammar School, City College Birmingham, CTC Kingshurst Academy, and University College Birmingham for their participation in our research.

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N2 - The pattern-based sequence classification system (PBSC) identifies regularly occurring patterns in sequential data and uses these patterns to predict meta-information. To illustrate the wide applicability of this approach, we classify speech-accompanying gestures produced by adults in order to predict their level of empathy. Previous research that focused on isolated gestures has shown that the frequency with which individuals produce certain speech-accompanying gestures is related to empathy. The current research extends these analyses by investigating the relationship between multi-gesture sequences and empathy. Patterns found in multi-gesture sequences prove to be more useful for predicting empathy levels in adults than patterns found in single gestures. This paper thus demonstrates that sequences of gestures contain additional information compared to gestures in isolation. More importantly, this study introduces PBSC as an effective method to incorporate time as an extra dimension in gestural communication, which can be extended to a wide range of sequential modalities.

AB - The pattern-based sequence classification system (PBSC) identifies regularly occurring patterns in sequential data and uses these patterns to predict meta-information. To illustrate the wide applicability of this approach, we classify speech-accompanying gestures produced by adults in order to predict their level of empathy. Previous research that focused on isolated gestures has shown that the frequency with which individuals produce certain speech-accompanying gestures is related to empathy. The current research extends these analyses by investigating the relationship between multi-gesture sequences and empathy. Patterns found in multi-gesture sequences prove to be more useful for predicting empathy levels in adults than patterns found in single gestures. This paper thus demonstrates that sequences of gestures contain additional information compared to gestures in isolation. More importantly, this study introduces PBSC as an effective method to incorporate time as an extra dimension in gestural communication, which can be extended to a wide range of sequential modalities.

M3 - Published conference contribution

SN - 978-0-9911967-2-2

SP - 124

EP - 129

BT - Proceedings of the 37th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

PB - Cogsci

T2 - 37th Annual Meeting of the Cognitive Science Society

Y2 - 22 July 2015 through 22 July 2015

ER -

Applying Pattern-based Classification to Sequences of Gestures

Abstract

Conference

Bibliographical note

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