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Data fusion analysis for attention‐deficit hyperactivity disorder emotion recognition with thermal image and Internet of Things devices
Author(s) -
Lai Ying Hsun,
Chang Yao Chung,
Tsai Chia Wei,
Lin Chih Hsun,
Chen Mu Yen
Publication year - 2021
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2866
Subject(s) - attention deficit hyperactivity disorder , intervention (counseling) , psychology , internet of things , the internet , emotion recognition , facial expression , affect (linguistics) , attention deficit , reinforcement learning , facial recognition system , cognitive psychology , artificial intelligence , computer science , clinical psychology , pattern recognition (psychology) , internet privacy , psychiatry , world wide web , communication
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.