Action-Based ADHD Diagnosis in Video
This work addresses early ADHD diagnosis to improve quality of life, but it is incremental as it applies existing action recognition methods to a new medical application.
The paper tackles ADHD diagnosis by introducing a video-based frame-level action recognition network for the first time, achieving results with a real multi-modal ADHD dataset that includes three action classes extracted from video.
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.