Action-Based ADHD Diagnosis in Video

TL;DR

This paper introduces a novel video-based action recognition network for diagnosing ADHD, utilizing a new multi-modal dataset to improve diagnosis accuracy while reducing equipment and staffing costs.

Contribution

It presents the first use of video-based frame-level action recognition for ADHD diagnosis and provides a new multi-modal dataset for research.

Findings

Effective action recognition for ADHD diagnosis demonstrated

Reduced equipment and staffing requirements compared to existing methods

Dataset made available for future research

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) causes significant impairment in various domains. Early diagnosis of ADHD and treatment could significantly improve the quality of life and functioning. Recently, machine learning methods have improved the accuracy and efficiency of the ADHD diagnosis process. However, the cost of the equipment and trained staff required by the existing methods are generally huge. Therefore, we introduce the video-based frame-level action recognition network to ADHD diagnosis for the first time. We also record a real multi-modal ADHD dataset and extract three action classes from the video modality for ADHD diagnosis. The whole process data have been reported to CNTW-NHS Foundation Trust, which would be reviewed by medical consultants/professionals and will be made public in due course.