Attention-AI: AI-Enabled Sensing for Joint Attention Detection in Children with Autism

Project: Applied Research

Project Details

Abstract

Attention is a form of behavioral and cognitive processing that gives the ability to focus and concentrate on tasks or stimuli. It plays a crucial role in learning, academic performance, and overall development. Children with good attention skills can sustain their focus, ignore distractions, and switch their attention when needed. On the contrary, attention in children with autism spectrum disorder (ASD) can be a complex phenomenon. ASD is a neurodevelopmental disorder characterized by challenges in social interaction, communication difficulties, and repetitive or restricted behavior patterns. Children with ASD are often reluctant to engage in social relationships and experience delays in cognitive and language development that affect their ability to develop social skills and play, learn through imitation, and benefit from learning situations. Joint attention involves the ability to share attention and engage in reciprocal interactions with others, and is a crucial foundation for social and cognitive development. It serves as a fundamental building block for language acquisition, social bonding, and overall adaptive functioning. However, individuals with autism often face challenges in establishing and maintaining joint attention, leading to significant impairments in social communication and interaction. Thus, the automatic detection of joint attention in autism is of paramount importance. Conventional approaches often rely on skilled professionals and are focused on psychology methods; however, this method is not scalable. Because autism primarily manifests in the behavior of children, we aim to develop a novel approach to the detection of joint attention through AI-enabled sensing technology. By focusing on i) how joint detection manifests in autistic children behavior, ii) developing an intelligent method for detection of joint attention in autism, we can gain valuable insights into the underlying mechanisms and develop targeted interventions to enhance social skills and improve the quality of life for individuals on the autism spectrum. This grant aims to leverage audio, visual and eye-tracking technology to encompass the behavior profile of children while engaged in activities specifically designed to require joint attention. AI and computer vision methods will be exploited to analyze the multimodal sensing data to develop an objective, non-contact, and contextual approach for the detection of joint attention behaviors in children who have autism. The outcomes of this grant will advance our understanding of joint attention in autism, leading to the development of effective assessment tools, early intervention strategies, and personalized therapies that promote social engagement and meaningful connections for individuals with autism.

Submitting Institute Name

Hamad Bin Khalifa University (HBKU)
Sponsor's Award NumberARG01-0508-230097
Proposal IDEX-QNRF-ARG-19
StatusActive
Effective start/end date1/04/241/04/27

Collaborative partners

Primary Theme

  • None

Primary Subtheme

  • None

Secondary Theme

  • None

Secondary Subtheme

  • None

Keywords

  • Autism spectrum disorder
  • Artificial intelligence
  • Non-contact sensing

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