# Skeleton-based action analysis for ADHD diagnosis

**Authors:** Yichun Li, Yi Li, Rajesh Nair, Syed Mohsen Naqvi  
**arXiv:** [2304.09751](https://arxiv.org/abs/2304.09751)  
**Date:** 2023-04-14  
**Summary page:** https://www.summarizepaper.com/fr/paper/2304.09751v1/

## 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.