  Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral
disorder worldwide. While extensive research has focused on machine learning
methods for ADHD diagnosis, most research relies on high-cost equipment, e.g.,
MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the
action characteristics of ADHD are desired. Skeleton-based action recognition
has gained attention due to the action-focused nature and robustness. In this
work, we propose a novel ADHD diagnosis system with a skeleton-based action
recognition framework, utilizing a real multi-modal ADHD dataset and
state-of-the-art detection algorithms. Compared to conventional methods, the
proposed method shows cost-efficiency and significant performance improvement,
making it more accessible for a broad range of initial ADHD diagnoses. Through
the experiment results, the proposed method outperforms the conventional
methods in accuracy and AUC. Meanwhile, our method is widely applicable for
mass screening.
