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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