Data fusion analysis for attention‐deficit hyperactivity disorder emotion recognition with thermal image and Internet of Things devices. (29th June 2020)

• Record Type: Journal Article
• Authors: Lai, Ying Hsun; Chang, Yao Chung; Tsai, Chia Wei; Lin, Chih Hsun; Chen, Mu Yen
• Other Names: Baker Thar guestEditor.; Al‐Jumeily Dhiya guestEditor.; Maamar Zakaria guestEditor.; Tari Zahir guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 3(2021)
• Page Start: 595
• Page End: 606
• Publication Date: 2020-06-29
• Subjects: attention‐deficit hyperactivity disorder -- data fusion -- emotion recognition; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2866
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Attention‐deficit hyperactivity disorder (ADHD) is a symptom of behavioral or emotional problems as these problems affect children's learning and social integration. With the advancements in the Internet of Things (IoTs), emotions can be detected through image and physiological data. However, some critical ADHD children are often accompanied by the inability to control their body and even facial expressions, making emotion recognition technologies difficult to develop successfully. This study aims to predict the emotions of ADHD children and to address their emotional problems with related IoT robotic devices. Data fusion analysis technology for facial expressions was used to combine thermal images and recognition data, while deep reinforcement learning technology was used to periodically stream information for ADHD students, in alignment with intervention strategies that were designed to address behavioral problems.