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Title page for ETD etd-01202009-121746

Type of Document Dissertation
Author Liu, Changchun
URN etd-01202009-121746
Title Physiology-based affect recognition and adaptation in human-machine interaction
Degree PhD
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Nilanjan Sarkar Committee Chair
George E. Cook Committee Member
Mitch Wilkes Committee Member
Richard Shiavi Committee Member
Zachary E. Warren Committee Member
  • Human machine interaction
  • physiology
  • emotion
  • Autism in children -- Treatment
  • Affect (Psychology)
  • Emotions -- Physiological aspects
  • Human-computer interaction
  • human robot interaction
  • affect computing
  • Robotics -- Human factors
Date of Defense 2009-01-19
Availability unrestricted
Recent advances in robotics and intelligent systems are expected to usher in a new era where the need for machines to “understand” humans becomes increasingly important. It should permit more meaningful and natural human-machine interaction (HMI) when a robot/computer can detect the affective cues of the person it is working with. The objective of this work is to investigate the following hypotheses for achieving an affect-sensitive HMI: (i) It is possible to detect the affective states of interest by using multiple indices derived from physiological signals in real-time; (ii) Such affective cues can be integrated within a machine's control architecture to make it capable of responding to them appropriately; and (iii) Such affect-sensitive systems are expected to improve the overall human-machine interaction experience. In this work, a systematic comparison of the strengths and weaknesses of machine learning methods was performed when they were employed for the physiology-based affect recognition. The impacts of the affect-sensitive closed-loop interaction were investigated in both human-robot interaction (HRI) and human-computer interaction (HCI) contexts. Furthermore, in response to the growing need for developing robot/computer assisted autism intervention systems for children with autism spectrum disorder (ASD), physiology-based affective modeling and adaptation methods were investigated for this specific population. Finally, physiology-based affective modeling using active learning for children with ASD was discussed.
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