A gesture recognition system for detecting behavioral patterns of ADHD

Miguel Ángel Bautista, Antonio Hernández-Vela, Sergio Escalera, Laura Igual, Oriol Pujol, Josep Moya, Verónica Violant, María T. Anguera

    Research output: Contribution to journalArticleResearchpeer-review

    31 Citations (Scopus)


    © 2015 IEEE. We present an application of gesture recognition using an extension of dynamic time warping (DTW) to recognize behavioral 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 Gaussian mixture models or an approximation of convex hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intraclass gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioral patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multimodal dataset (RGB plus depth) of ADHD children recordings with behavioral patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.
    Original languageEnglish
    Article number7047782
    Pages (from-to)136-147
    JournalIEEE Transactions on Cybernetics
    Issue number1
    Publication statusPublished - 1 Jan 2016


    • Attention deficit hyperactivity disorder (ADHD)
    • Convex hulls
    • Dynamic time warping (DTW)
    • Gaussian mixture models (GMMs)
    • Gesture recognition
    • Multimodal RGB-depth data


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