Harnessing Video Intelligence: Intelligent System for ADHD Detection

Attention Deficit Hyperactivity Disorder (ADHD) causes significant impairment in various domains. It is known in the Medical Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) that the symptoms of ADHD may manifest in actions and daily behaviors. Deep learning methods based on fMRI and EEG have improved the efficiency of the ADHD detection process. However, the cost of the specialized equipment and trained staff required by the existing methods are generally huge. Therefore, we introduce the action recognition network based on raw RGB videos to ADHD detection for the first time. We also extract corresponding action characteristics with two proposed novel measurements: Attention Deficit Ratio (R
AD
) and Stationary Ratio (R
S
) based on the action features of ADHD. The two-stage final ADHD detection is decided with R
AD
and R
S
fusion and achieves a high accuracy of 95.5% in our real multimodal dataset. The dataset recorded in our Intelligent Sensing Laboratory has been processed and reported to CNTW-NHS Foundation Trust, which will be reviewed by medical consultants/professionals and to be made public in due course.