Machine learning classification of design team members' body language patterns for real time emotional state detection

Ishan Behoora, Conrad S. Tucker

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

Design team interactions are one of the least understood aspects of the engineering design process. Given the integral role that designers play in the engineering design process, understanding the emotional states of individual design team members will help us quantify interpersonal interactions and how those interactions affect resulting design solutions. The methodology presented in this paper enables automated detection of individual team member's emotional states using non-wearable sensors. The methodology uses the link between body language and emotions to detect emotional states with accuracies above 98%. A case study involving human participants, enacting eight body language poses relevant to design teams, is used to illustrate the effectiveness of the methodology. This will enable researchers to further understand design team interactions.

Original languageEnglish (US)
Article number734
Pages (from-to)100-127
Number of pages28
JournalDesign Studies
Volume39
DOIs
StatePublished - Jul 1 2015

All Science Journal Classification (ASJC) codes

  • Architecture
  • Arts and Humanities (miscellaneous)
  • Engineering(all)
  • Social Sciences(all)
  • Computer Science Applications
  • Artificial Intelligence

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