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.

中文翻译：

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通过热图像和物联网设备进行的注意力缺陷多动障碍情感识别的数据融合分析

注意缺陷多动障碍（ADHD）是行为或情感问题的症状，因为这些问题影响儿童的学习和社会融合。随着物联网（IoT）的发展，可以通过图像和生理数据检测情绪。但是，一些严重的多动症儿童常常伴随着无法控制自己的身体甚至面部表情的情况，这使得情感识别技术难以成功开发。这项研究旨在预测多动症儿童的情绪，并通过相关的物联网机器人设备解决他们的情绪问题。用于面部表情的数据融合分析技术用于组合热图像和识别数据，而深度强化学习技术用于为ADHD学生定期流式传输信息，