Applying Pattern-based Classification to Sequences of Gestures

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

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

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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 languageEnglish
Title of host publicationProceedings of the 37th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Number of pages6
ISBN (Print)978-0-9911967-2-2
Publication statusPublished - 2015
Event37th Annual Meeting of the Cognitive Science Society - Pasadena, California, United States
Duration: 22 Jul 201522 Jul 2015


Conference37th Annual Meeting of the Cognitive Science Society
Country/TerritoryUnited States

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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