Skeleton-based action analysis for ADHD diagnosis

• URL: http://arxiv.org/abs/2304.09751v1
• Date: Fri, 14 Apr 2023 13:07:27 GMT
• Title: Skeleton-based action analysis for ADHD diagnosis
• Authors: Yichun Li, Yi Li, Rajesh Nair, Syed Mohsen Naqvi
• Abstract summary: We propose a novel ADHD diagnosis system with a skeleton-based action recognition framework. Compared to conventional methods, the proposed method shows cost-efficiency and significant performance improvement. Our method is widely applicable for mass screening.
• Score: 10.393047508477173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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