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We present an application of gesture recognition using an extension ofDynamic Time Warping (DTW) to recognize behavioural patterns of AttentionDeficit Hyperactivity Disorder (ADHD). We propose an extension of DTW usingone-class classifiers in order to be able to encode the variability of agesture category, and thus, perform an alignment between a gesture sample and agesture class. We model the set of gesture samples of a certain gesturecategory using either GMMs or an approximation of Convex Hulls. Thus, we add atheoretical contribution to classical warping path in DTW by including localmodeling of intra-class gesture variability. This methodology is applied in aclinical context, detecting a group of ADHD behavioural patterns defined byexperts in psychology/psychiatry, to provide support to clinicians in thediagnose procedure. The proposed methodology is tested on a novel multi-modaldataset (RGB plus Depth) of ADHD children recordings with behavioural patterns.We obtain satisfying results when compared to standard state-of-the-artapproaches in the DTW context.

A Gesture Recognition System for Detecting Behavioral Patterns of ADHD