ADHD diagnosis based on action characteristics recorded in videos using machine learning

TL;DR

This paper presents a new machine learning-based action recognition system analyzing videos from multiple cameras to diagnose ADHD by identifying characteristic behaviors related to attention and hyperactivity.

Contribution

It introduces a novel multi-camera video analysis method and a machine learning system for ADHD diagnosis based on action recognition neural networks.

Findings

Effective identification of ADHD-related behaviors from video recordings

First implementation of a neural network-based ADHD diagnosis system

Proposed classification criteria for ADHD diagnosis based on action features

Abstract

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.