Skeleton-based action analysis for ADHD diagnosis

TL;DR

This paper introduces a cost-effective, skeleton-based action recognition system for ADHD diagnosis that outperforms traditional methods in accuracy and is suitable for mass screening, using a multi-modal dataset.

Contribution

The paper presents a novel ADHD diagnosis framework leveraging skeleton-based action recognition with state-of-the-art detection algorithms, improving accuracy and accessibility.

Findings

Outperforms conventional methods in accuracy and AUC

Cost-efficient and suitable for mass screening

Utilizes a real multi-modal ADHD dataset

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.