Improvement of ADHD Behaviors with AI Perception Technology

  • Ying Hsun LaiEmail author
  • Yao Chung Chang
  • Yi Wei Ma
  • Shih Yun Huang
  • Han Chieh Chao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1227)


Attention Deficit Hyperactivity Disorder (ADHD) is an attention deficit disorder that includes excessive activity and impulsive symptoms in children, which causes behavioral, emotional, and related learning disabilities, and it takes considerable time for teachers and parents to counsel such children. This study aims to introduce AI perception technology to assist teachers in solving the behavioral problems of children with ADHD. The AI perception technology was introduced into the perceptual system to assist tutors to record children’s functional assessments in the early and middle stages, in order to improve the time spent by tutors in implementing behavioral functional observation and assessment, and to solve the difficulties of assessments caused mostly by post observations rather than direct observation, thus, tutors can record the functional assessment scale more accurately and formulate relevant treatment strategies for the children. During the intervention, AI perception is used to observe the emotions and attention of the schoolchildren, in order that the real-time strategies can be provided when schoolchildren are unconscious of their emotions or fail to focus on learning, and the effectiveness of the strategies are recorded with the help of interactive robots to identify the best assistant processing strategies and construct the best personalized strategy activities to assist instructors to improve the students’ behavioral problems and emotional control in the classroom. This study has closely examined the effect of this system on the behavioral problems of ADHD children.


ADHD Behavioral problems AI perception technology 



The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan for supporting this research under Contract MOST 107-2511-H-143-004 –.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ying Hsun Lai
    • 1
    Email author
  • Yao Chung Chang
    • 1
  • Yi Wei Ma
    • 2
  • Shih Yun Huang
    • 3
  • Han Chieh Chao
    • 3
  1. 1.Department of Computer Science and Information EngineeringNational Taitung UniversityTaitungTaiwan
  2. 2.Department of Electrical EngineeringNational Taiwan University of Science and TechnologyTaitungTaiwan
  3. 3.Department of Electrical EngineeringNational Dong Hwa UniversityHualienTaiwan

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