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Human Affective State Recognition and Classification During Human-Robot Interaction Scenarios

[+] Author Affiliations
Zhe Zhang

State University of New York (SUNY) at Stony Brook, Stony Brook, NY

Goldie Nejat

University of Toronto, Toronto, ON, Canada

Paper No. DETC2009-87647, pp. 435-441; 7 pages
doi:10.1115/DETC2009-87647
From:
  • ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: ASME/IEEE 2009 International Conference on Mechatronic and Embedded Systems and Applications; 20th Reliability, Stress Analysis, and Failure Prevention Conference
  • San Diego, California, USA, August 30–September 2, 2009
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4900-2 | eISBN: 978-0-7918-3856-3
  • Copyright © 2009 by ASME

abstract

A new novel breed of robots known as socially assistive robots is emerging. These robots are capable of providing assistance to individuals through social and cognitive interaction. The development of socially assistive robots for health care applications can provide measurable improvements in patient safety, quality of care, and operational efficiencies by playing an increasingly important role in patient care in the fast pace of crowded clinics, hospitals and nursing/veterans homes. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one main challenge in the development of intelligent socially assistive robots: The robot’s ability to identify, understand and react to human intent and human affective states during assistive interaction. In particular, we present a unique non-contact and non-restricting sensory-based approach for identification and categorization of human body language in determining the affective state of a person during natural real-time human-robot interaction. This classification allows the robot to effectively determine its taskdriven behavior during assistive interaction. Preliminary experiments show the potential of integrating the proposed gesture recognition and classification technique into intelligent socially assistive robotic systems for autonomous interactions with people.

Copyright © 2009 by ASME

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