Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder |
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| Authors: | Changchun Liu Karla Conn Nilanjan Sarkar Wendy Stone |
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| Affiliation: | 1. Department of Electrical Engineering and Computer Science, Vanderbilt University, VU Station B 351679, 2301 Vanderbilt Place, Nashville, TN 37235-1679, USA;2. Department of Mechanical Engineering, VU Station B 351592, 2301 Vanderbilt Place, Nashville, TN 37235, USA;3. Vanderbilt Treatment and Research Institute for Autism Spectrum Disorders, 1207 18th Avenue South, Nashville, TN 37212, USA;4. Vanderbilt Kennedy Center, Department of Pediatrics, 1207 18th Avenue South, Nashville, TN 37212, USA;1. Department of Computer Science Center for Scientific Research and Higher Education, CICESE Ensenada, Mexico, 3918 Carretera Ensenada-Tijuana, Ensenada, B.C. 22860, Mexico;2. Educational Psychology School, CETYS Universidad Campus Mexicali, Calz. Cetys S/N, Rivera, Mexicali, B.C. 21259, Mexico;1. C/O- Mr NK Mishra, N-1/256, Nayapalli, IRC Village, 751015 Bhubaneswar, Odisha, India;2. Regional Medical research Centre, Chandrasekharpur, 751023 Bhubaneswar, Odisha, India;1. Center for Neurodevelopment and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland;2. Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, Maryland;3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland;4. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland;5. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland;6. School of Psychology, University of Nottingham, Nottingham, United Kingdom;7. Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom;8. Joint Doctoral Program in Language and Communicative Disorders, San Diego State University and University of California San Diego, San Diego, California;1. Department of Psychology, Virginia Tech, United States;2. Department of Psychiatry, University of Pittsburgh School of Medicine, United States;3. Department of Psychiatry, University of North Carolina, United States;4. Department of Psychology, University of North Carolina, United States;5. Virginia Tech Carilion Research Institute and Department of Psychology, Virginia Tech, United States;1. Department of Development Psychology and Teaching, University of Alicante, Alicante, Spain;2. Department of Physics, Systems Engineering and Sign Theory, University of Alicante, Spain;3. Department of General and Specific Didactics, University of Alicante, Alicante, Spain;1. Department of Psychiatry, China Medical University Hsinchu Hospital, China Medical University, Hsinchu, Taiwan;2. Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan;3. Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan;4. Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan;5. Department of Dermatology, School of Medicine and College of Medicine, Taipei Medical University, Taipei, Taiwan;6. Department of Psychiatry, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan;7. Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan;8. Department of Psychiatry, College of Medicine, National Taiwan University, Taipei, Taiwan;9. Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan |
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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. |
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| Keywords: | Human– computer Interaction Autism intervention Physiological sensing Support vector machines Affect recognition |
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