A Gesture Recognition System for Detecting Behavioral Patterns of ADHD
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...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Institut d'Investigació i Innovació Parc Taulí (I3PT) |
| Repositorio: | r-I3PT. Repositorio Institucional Producción Científica del Institut d'Investigació i Innovació Parc Taulí |
| OAI Identifier: | oai:i3pt.fundanetsuite.com:p5675 |
| Acceso en línea: | https://i3pt.portalinvestigacion.com/publicaciones/5675 |
| Access Level: | acceso abierto |
| Palabra clave: | Attention deficit hyperactivity disorder (ADHD) convex hulls dynamic time warping (DTW) Gaussian mixture models (GMMs) gesture recognition multimodal RGB-depth data |
| Sumario: | 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. |
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