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Responses during Facial Emotional Expression Recognition Tasks Using Virtual Reality and Static IAPS Pictures for Adults with Schizophrenia

  • Esubalew Bekele
  • Dayi Bian
  • Zhi Zheng
  • Joel Peterman
  • Sohee Park
  • Nilanjan Sarkar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8526)

Abstract

Technology-assisted intervention has the potential to adaptively individualize and improve outcomes of traditional schizophrenia (SZ) intervention. Virtual reality (VR) technology, in particular, has the potential to simulate real world social and communication interactions and hence could be useful as a therapeutic platform for SZ. Emotional face recognition is considered among the core building blocks of social communication. Studies have shown that emotional face processing and understanding is impaired in patients with SZ. The current study develops a novel VR-based system that presents avatars that can change their facial emotion dynamically for emotion recognition tasks. Additionally, this system allows real-time measurement of physiological signals and eye gaze during the emotion recognition tasks, which can be used to gain insight about the emotion recognition process in SZ population. This study further compares VR-based facial emotion recognition with that of the more traditional emotion recognition from static faces using a small usability study. Results from the usability study suggest that VR could be a viable platform for SZ intervention and implicit signals such as physiological signals and eye gaze can be utilized to better understand the underlying pattern that is not available from user reports and performance alone.

Keywords

facial expression emotion recognition virtual reality IAPS adaptive interaction eye tracking physiological processing schizophrenia intervention 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Esubalew Bekele
    • 1
  • Dayi Bian
    • 1
  • Zhi Zheng
    • 1
  • Joel Peterman
    • 2
  • Sohee Park
    • 2
  • Nilanjan Sarkar
    • 1
    • 3
  1. 1.Electrical Engineering and Computer Science DepartmentVanderbilt UniversityNashvilleUSA
  2. 2.Psychology DepartmentVanderbilt UniversityNashvilleUSA
  3. 3.Mechanical Engineering DepartmentVanderbilt UniversityNashvilleUSA

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