Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder

  • Authors:

  • Changchun Liu;Karla Conn;Nilanjan Sarkar;Wendy Stone

  • Affiliations:

  • Department of Electrical Engineering and Computer Science, Vanderbilt University, VU Station B 351679, 2301 Vanderbilt Place, Nashville, TN 37235-1679, USA;Department of Electrical Engineering and Computer Science, Vanderbilt University, VU Station B 351679, 2301 Vanderbilt Place, Nashville, TN 37235-1679, USA;Department of Mechanical Engineering, VU Station B 351592, 2301 Vanderbilt Place, Nashville, TN 37235, USA and Department of Electrical Engineering and Computer Science, Vanderbilt University, VU ...;Vanderbilt Treatment and Research Institute for Autism Spectrum Disorders, 1207 18th Avenue South, Nashville, TN 37212, USA and Vanderbilt Kennedy Center, Department of Pediatrics, 1207 18th Avenu ...

  • Venue:
  • International Journal of Human-Computer Studies
  • Year:
  • 2008

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Abstract

Generally, an experienced therapist continuously monitors the affective cues of the children with Autism Spectrum Disorders (ASD) and adjusts the course of the intervention accordingly. In this work, we address the problem of how to make the computer-based ASD intervention tools affect-sensitive by designing therapist-like affective models of the children with ASD based on their physiological responses. Two computer-based cognitive tasks are designed to elicit the affective states of liking, anxiety, and engagement that are considered important in autism intervention. A large set of physiological indices are investigated that may correlate with the above affective states of children with ASD. In order to have reliable reference points to link the physiological data to the affective states, the subjective reports of the affective states from a therapist, a parent, and the child himself/herself were collected and analyzed. A support vector machines (SVM)-based affective model yields reliable prediction with approximately 82.9% success when using the therapist's reports. This is the first time, to our knowledge, that the affective states of children with ASD have been experimentally detected via physiology-based affect recognition technique.