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Recognizing social gestures with a wrist-worn smartband

  • Jonathan Knighten
  • , Stephen McMillan
  • , Tori Chambers
  • , Jamie Payton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The ability to recognize social gestures opens the door for the development of enhanced pervasive computing applications that are responsive to users' social interactions. In this paper, we explore the feasibility of using a smartband for social gesture recognition. We apply logistic regression, a supervised machine learning technique, to accelerometer data collected in a study of 32 users performing 12 social gestures. Our experimental results show promise for recognizing social gestures with a smartband; our simple approach achieves an average accuracy of 86% for classification of social gestures.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages544-549
Number of pages6
ISBN (Electronic)9781479984251
DOIs
StatePublished - Jun 24 2015
Externally publishedYes
Event13th IEEE International Conference on Pervasive Computing and Communication, PerCom Workshops 2015 - St. Louis, United States
Duration: Mar 23 2015Mar 27 2015

Publication series

Name2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015

Conference

Conference13th IEEE International Conference on Pervasive Computing and Communication, PerCom Workshops 2015
Country/TerritoryUnited States
CitySt. Louis
Period3/23/153/27/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction
  • Health(social science)

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