基于骨骼的动作分析用于 ADHD 诊断
Skeleton-based action analysis for ADHD diagnosis
AI总结：
针对 ADHD 诊断设备成本高的问题，提出基于骨骼动作识别框架的低成本诊断系统，并在真实多模态数据集上验证其准确率和 AUC 优于传统方法。
摘要：
注意缺陷多动障碍（ADHD）是一种在全球范围内常见的神经行为障碍。尽管已有大量研究关注用于 ADHD 诊断的机器学习方法，但多数研究依赖高成本设备，例如 MRI 机器和 EEG 贴片。因此，人们期望获得基于 ADHD 动作特征的低成本诊断方法。基于骨骼的动作识别因其以动作为核心的特性和鲁棒性而受到关注。在本研究中，我们提出了一种新颖的 ADHD 诊断系统，该系统采用基于骨骼的动作识别框架，利用真实的多模态 ADHD 数据集和最先进的检测算法。与传统方法相比，所提出的方法展现出成本效益和显著的性能提升，使其更易于用于广泛的 ADHD 初步诊断。实验结果表明，所提出方法在准确率和 AUC 方面优于传统方法。同时，我们的方法可广泛应用于大规模筛查。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
