NDLI: Online Affect Detection and Adaptation in Robot Assisted Rehabilitation for Children with Autism

Content Provider	IEEE Xplore Digital Library
Author	Changchun Liu Conn, K. Sarkar, N. Stone, W.
Copyright Year	2007
Description	Author affiliation: Vanderbilt Univ., Nashville (Changchun Liu; Conn, K.; Sarkar, N.)
Abstract	This paper presents a novel affect-sensitive human-robot interaction framework for rehabilitation of children with autism spectrum disorder (ASD) where the robot can detect the affective cues of the children implicitly and response to them appropriately. Psychophysiological analysis is performed that uses subjective reports of the affective states from a clinical observer. Comprehensive physiological indices are investigated that may correlate with the affective states of children with ASD. A robot uses a support vector machines based affect model to detect the affective cues. A reinforcement learning based adaptation mechanism is employed to allow the robot to adjust its behaviors autonomously as a function of the predicted children's affective state. Four adolescents diagnosed with high-functioning autism participated in the experiments. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via physiology-based affective modeling technique in real-time. This is also the first time that impact of affect-sensitive interaction between a robot and children with ASD in closed loop has been demonstrated experimentally.
Starting Page	588
Ending Page	593
File Size	4569955
Page Count	6
File Format	PDF
ISBN	9781424416349
DOI	10.1109/ROMAN.2007.4415153
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-26
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Rehabilitation robotics Autism Variable speed drives Pediatrics Human robot interaction Robot sensing systems Application software Educational robots Computer science Mechanical engineering Affective Computing Human-robot Interaction Autism Intervention Rehabilitation Physiological Sensing
Content Type	Text
Resource Type	Article

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