TY  - JOUR
AU  - Miguel Angel Bautista
AU  - Antonio Hernandez
AU  - Sergio Escalera
AU  - Laura Igual
AU  - Oriol Pujol
AU  - Josep Moya
AU  - Veronica Violant
AU  - Maria Teresa Anguera
PY  - 2016//
TI  - A Gesture Recognition System for Detecting Behavioral Patterns of ADHD
T2  - TSMCB
JO  - IEEE Transactions on System, Man and Cybernetics, Part B
SP  - 136
EP  - 147
VL  - 46
IS  - 1
KW  - Gesture Recognition
KW  - ADHD
KW  - Gaussian Mixture Models
KW  - Convex Hulls
KW  - Dynamic Time Warping
KW  - Multi-modal RGB-Depth data
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/BHE2015.pdf
UR  - http://dx.doi.org/10.1109/TCYB.2015.2396635
N1  - HuPBA; MILAB;
ID  - Miguel Angel Bautista2016
ER  -