ADHD diagnosis based on action characteristics recorded in videos using machine learning
This work addresses the timely diagnosis of ADHD for patients and clinicians, but it appears incremental as it applies existing action recognition methods to a new medical application.
The paper tackled the problem of increasing demand for ADHD diagnosis by developing a machine learning system that analyzes raw video recordings to diagnose ADHD based on action characteristics, achieving a novel implementation of action recognition neural networks for this purpose.
Demand for ADHD diagnosis and treatment is increasing significantly and the existing services are unable to meet the demand in a timely manner. In this work, we introduce a novel action recognition method for ADHD diagnosis by identifying and analysing raw video recordings. Our main contributions include 1) designing and implementing a test focusing on the attention and hyperactivity/impulsivity of participants, recorded through three cameras; 2) implementing a novel machine learning ADHD diagnosis system based on action recognition neural networks for the first time; 3) proposing classification criteria to provide diagnosis results and analysis of ADHD action characteristics.