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Author (up) Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera edit   pdf
doi  openurl
  Title A Gesture Recognition System for Detecting Behavioral Patterns of ADHD Type Journal Article
  Year 2016 Publication IEEE Transactions on System, Man and Cybernetics, Part B Abbreviated Journal TSMCB  
  Volume 46 Issue 1 Pages 136-147  
  Keywords Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data  
  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.  
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  Area Expedition Conference  
  Notes HuPBA; MILAB; Approved no  
  Call Number Admin @ si @ BHE2016 Serial 2566  
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