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ADHD indicators modelling based on Dynamic Time Warping from RGB data: A feasibility study Antonio Hernández-Vela, Miguel Reyes, Laura Igual, Josep Moya, Verónica Violant, and Sergio Escalera

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ADHD: Attention deficit hyperactivity disorder 2 InattentionHyperactivityImpulsivity

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Outline 1.Introduction 2.Methodology 3.Results 4.Conclusion 3

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Introduction Video-based behavior analysis for ADHD diagnosis in children between 8-11 years. 4

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Introduction Behavior analysis Human pose information along time 5 Head Body Hands time Gestures Inattention Hyperactivity Impulsivity 1. Data acquisition 2. Feature extraction: Human Pose 3. Gesture detection

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Outline 1.Introduction 2.Methodology 1.Data acquisition 2.Feature extraction 3.Gesture detection 3.Results 4.Conclusion 6

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Data aqcuisition 7 Microsoft’s Kinect RGB + Depth Invariant to color, texture and lighting conditions Human pose directly obtained

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Feature extraction: Human Pose 8 RGB + Depth Body skeleton 42-dimensional vector: 14 joints × 3 spatial dimensions

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Gesture detection 9 Dynamic Time Warping (DTW)

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Threshold computing 10 Leave-one-out similarity measure between different samples and gestures G1 G11G12…G13 G2 G21G22…G23 … Gn Gn1Gn2Gn3 G1 1 Different gestures Different samples

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Outline 1.Introduction 2.Methodology 3.Results 4.Conclusion 11

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Results 12

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Results 13

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Outline 1.Introduction 2.Methodology 3.Results 4.Conclusion 14

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Outline 15 1.Introduction 2.Methodology 3.Results 4.Conclusion

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Conclusion 16 Methodology for gesture segmentation and recognition at the same time. First results indicate the objectives are feasible. Future work: Automatic callibration Feature weighting (body joints)

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ADHD indicators modelling based on Dynamic Time Warping from RGB data: A feasibility study Antonio Hernández-Vela, Miguel Reyes, Laura Igual, Josep Moya, Verónica Violant, and Sergio Escalera Thank You! Questions?

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