HAI 2019

Workshop on

Clinical Use of Technology for Individuals with Autism Spectrum Disorder

6th October 2019 | Kyoto, Japan


Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. People with ASD have difficulty with communication and interaction with other people, restricted interests and repetitive behaviors, and symptoms that hurt the person’s ability to function properly in school, work, and other areas of life. ASD can cause lifelong challenges. However, there are a variety of therapeutic and educational approaches, any of which may have educational benefits in some but not all individuals with ASD. Thus, there is an urgent need for more efficacious treatments whose realistic applications will yield more substantive impacts on the neurodevelopmental trajectories of individuals with these disorders given the current resource-strained environments. Given recent rapid technological advances, it has been argued that specific technological applications could be effectively harnessed to provide innovative clinical treatments for individuals with ASD. There have, however, been few exchanges between psychiatrists and informatics researchers. Recently, exchanges between psychiatrists and informatics researchers are now beginning to occur. In this workshop, to promote a world-wide interdisciplinary discussion about the potential technological applications for ASD’s fields, pioneering research activities for patients are introduced not only by psychiatrists but also engineers.

Call for Papers

We welcome submission on all topics related to clinical use of technology for individuals with autism spectrum disorder. Papers should be formatted according to the SCM SIGCHI Extended Abstracts Format. The following are the templates: Latex, MS Word. Papers should be a minimum of two pages and no more than four pages in length. All submitted papers will be reviewed by at least two reviewers.

Please submit papers via email to: t4asd@ai.info.gifu-u.ac.jp

Important Dates

  • 16 Sep 2019: Paper Submission
  • 23 Sep 2019: Notification of Acceptance
  • 30 Sep 2019: Final Camera-ready Papers Due


Prof. Zachary Warren

School of Medicine, Vanderbilt University

Title: Intelligent Use of Intelligent Technology: Towards autonomous ‘social’ systems of meaning

Abstract: There has been growing interest in utilizing intelligent technology for clinical use in autism spectrum disorder (ASD) – from enhancing detection to social intervention. Researchers have hypothesized that technological tools may be particularly promising as intervention mechanisms, given that many with ASD (1) may better understand physical and visual worlds relative to social worlds, (2) respond well to technologically cued feedback, and (3) show intrinsic interests in technology (e.g. robots, VR, etc.). In addition, researchers have documented numerous systemic and resource barriers towards effective identification and treatment that intelligent technology may be able to overcome (e.g. limited specialists, geographic limits, financial/cost barriers). Unfortunately, to date most clinical applications of technology for ASD have measured behavior in response to simple exposure to robots, toys, or screen-based interactions and enacted limited preprogrammed or confederate dependent interactions. If the ultimate goal of intelligent technology is to engage individuals in effective social intervention over time on a larger scale, meaningful ‘social’ technology may require an interactional framework that dynamically responds to small, meaningful behavioral shifts and links these back to ongoing interactions and learning. In this capacity, the development of dynamic, “closed-loop” interactions involving both technology and aspects of in vivo social interaction may be particularly powerful. “Closed-loop” refers to the ability of a technological system to dynamically interact with an individual with ASD in real-time (i.e. intelligent adaptation), as opposed to an “open-loop” system that behaves in a limited, pre-programmed way. This talk will review ongoing adaptive, “closed-loop” technologically mediated learning systems for ASD intervention. This includes the development and study of socially-relevant intelligent robotic learning environments capable of scaffolding early social orienting and joint attention skills in young children, VR systems for gaze-contingent interaction and driving skills in adolescents, intelligent screening technology for infants and toddlers (e.g. multisensory data capture systems, innovative screening tools), and collaborative virtual learning environments across lifespan activities (i.e. interview systems, social skills interventions, motor/academic interventions). The talk will also discuss and highlight the current challenges related to developing pragmatic, beneficial, and generalizable technological intervention systems for the targeted population (e.g. considerations of heterogeneity within and across individuals over time).

Prof. Yukie Nagai

International Research Center for Neurointelligence, The University of Tokyo

Title: Where do social difficulties come from?: Predictive coding account for autism

Abstract: Predictive coding has been proposed as a unified theory for the human brain. It suggests that the brain perceives the environment and acts on it so as to minimize prediction errors. It has been hypothesized that atypical cognitive characteristics of autism spectrum disorder (ASD) are induced by deficits in the prediction ability. Imbalance between top-down prediction and bottom-up sensation leads to the emergence of atypical internal models, resulting in difficulties in social communication.
My talk presents computational studies to investigate the predictive coding theory. We have been designing neural network models based on the theory and examining how modifications in the model parameters, which correspond to neural impairments in ASD, affect learning of the networks. Our experiments showed that a proper balance between top-down prediction and bottom-up sensation led to the emergence of well-structured internal models. Only such networks achieved higher generalization capabilities as observed in typically developed individuals. In contrast, weaker or stronger influence of prediction produced ASD- or ADHD-like behaviors. Neural networks with weaker predictions acquired rote memorization of given tasks whereas networks with stronger predictions tended to fail in achieving the tasks. Both types of networks exhibited poor adaptation capabilities as observed in ASD.
My talk next presents an HMD display that reproduces atypical visual perception in ASD. We revealed hyper- and hypo-sensitivitiess in ASD’s visual perception and modeled its perceptual process based on predictive coding. The HMD allows typically developed individuals to experience what perceptual impairments people with ASD have and how such impairments affect social disabilities through interaction in the environment. For example, perceptual noises in the vision make it difficult to identify other persons and communicate with them. Taken together, these studies suggest that communication difficulties in ASD are a secondary problem caused by a primary impairment in the prediction ability and that diversities in cognitive and social capabilities of ASD are accounted for by different biases in the predictive brain.


9:00-9:10 Introduction
9:10-10:00 Keynote: Zachary Warren (Vanderbilt University)
Intelligent Use of Intelligent Technology: Towards autonomous ‘social’ systems of meaning
10:00-10:50 Keynote: Yukie Nagai (The University of Tokyo)
Where do social difficulties come from?: Predictive coding account for autism
10:50-11:10 Break
11:10-11:30 Sao Mai Nguyen (IMT Atlantique), Nathalie Collot-Lavenne (Brest University Hospital), Christophe Lohr (IMT Atlantique), Sebastien Guillon (IMT Atlantique), Patricio Tula (IMT Atlantique), Alvaro Paez (IMT Atlantique), Mouad Bouaida (IMT Atlantique), Arthus Anin (IMT Atlantique), Saad El Qacemi (IMT Atlantique)
An implementation of an imitation game with ASD children to learn nursery rhymes
11:30-11:50 Junya Morita (Shizuoka University), Kazuki Itabashi (Shizuoka University)
Model-based reminiscence: a method of supporting memory integration for ASD individuals
11:50-12:10 Tetsuyou Watanabe (Kanazawa University)
The specific features of operating an unfamiliar robot for individuals with autism spectrum disorders
12:10-12:30 Soichiro Matsuda (Tsukuba University)
Social imaging for autism research
12:30-12:50 Yuichiro Yoshikawa (Osaka University)
Possibility of converation robots for individuals with Autism Spectrum Disorder
12:50-13:10 Hirokazu Kumazaki (National Center of Neurology and Psychiatry)
Potentiality of focusing on olfactory trait for autism spectrum disorders


  • Hirokazu Kumazaki, National Center of Neurology and Psychiatry
  • Kazunori Terada, Gifu University
  • Tetsuyou Watanabe, Kanazawa University
  • Yuichiro Yoshikawa, Osaka University