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.

本文提出了一种基于骨骼动作识别的注意力缺陷多动障碍诊断系统，利用真实的多模式 ADHD 数据集和先进的检测算法。与传统方法相比，该方法在成本效益和性能方面均表现出色，适用于广泛的初步 ADHD 诊断和大规模筛查。