Data fusion analysis for attention-deficit hyperactivity disorder emotion recognition with thermal image and Internet of Things devices

Ying Hsun Lai, Yao Chung Chang, Chia Wei Tsai, Chih Hsun Lin, Mu Yen Chen

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)595-606
Number of pages12
JournalSoftware - Practice and Experience
Volume51
Issue number3
DOIs
Publication statusPublished - 2021 Mar

All Science Journal Classification (ASJC) codes

  • Software

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