Attention analysis in interactive software for children with autism

  • Authors:
  • A. Ould Mohamed;V. Courboulay;K. Sehaba;M. Menard

  • Affiliations:
  • Université de La Rochelle, La Rochelle, France;Université de La Rochelle, La Rochelle, France;Université de La Rochelle, La Rochelle, France;Université de La Rochelle, La Rochelle, France

  • Venue:
  • Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
  • Year:
  • 2006

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Abstract

This work is a part of an ongoing project that focuses on potential applications of an interactive system that helps children with autism. Autism is classified as a neurodevelopmental disorder that manifests itself in markedly abnormal social interaction, communication ability, patterns of interests, and patterns of behavior [1]. Children with autism are socially impaired and usually do not attend to the people around them. An interesting point which characterized children with autism is that they are unable to choose which event is more or less important. As a consequence they are often saturated because of too many stimuli and thus they adopt an extremely repetitive, unusual, self-injurious, or aggressive behaviour. Recently, a new trend of using human computer interface (HCI) technology and computer science in the treatment of autism has emerged [2, 3]. The platform we developed helps children with autism to focus their attention on a specific task. In this article, we only present the attention analysis system which is a part of a more general system that used a multi-agent architecture [4]. Each task proposed on our system fit to each child, is reproducible and evolutive following a specific scenario defined by the expert. This scenario takes into account age, ability, and degree of autism of each child. In order to focus a child's attention onto the relevant object, our system displays or plays specific stimulus; once again the specific stimulus is defined for each child. Symbol or sound represents an emotional and satisfaction value for the child. The major problem is to define the correct moment when the system has to (dis)play this signal. We tackle this problem by defining a robust measure of attention. This measure is defined by analyzing the gaze direction and the face orientation, and incorporating the child's specific profile. Following expert directives, our system helps children to categorize elementary perception (strong, smooth, quick, slow, big, small...). Our objective is that children re-use these classifications in others situations.