A Gesture Recognition System for Detecting Behavioral Patterns of ADHD

TL;DR

This paper introduces a gesture recognition system using an extended Dynamic Time Warping method with one-class classifiers and local modeling to detect ADHD behavioral patterns, aiding clinical diagnosis.

Contribution

It proposes a novel extension of DTW incorporating intra-class variability modeling with GMMs or convex hulls for behavioral pattern recognition in ADHD.

Findings

Achieved satisfactory detection accuracy on a new multi-modal ADHD dataset.

Outperformed standard DTW-based approaches in recognizing ADHD behavioral patterns.

Abstract

We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.