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ADHD indicators modelling based on Dynamic Time Warping from RGB data: A feasibility study

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. ADHD: Attention deficit hyperactivity disorder. Inattention. Hyperactivity. Impulsivity.

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ADHD indicators modelling based on Dynamic Time Warping from RGB data: A feasibility study

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  1. ADHD indicatorsmodellingbasedonDynamic Time Warpingfrom RGB data: A feasibilitystudy Antonio Hernández-Vela, Miguel Reyes, Laura Igual, Josep Moya, Verónica Violant, and Sergio Escalera

  2. ADHD: Attentiondeficithyperactivitydisorder Inattention Hyperactivity Impulsivity

  3. Outline • Introduction • Methodology • Results • Conclusion

  4. Introduction • Video-based behavior analysis for ADHD diagnosis in children between 8-11 years. • Automatic detection of ADHD visual indicators

  5. Introduction • Behavior analysis  Human pose information along time Inattention Head Body Hands time Hyperactivity Gestures Impulsivity 2. Featureextraction: Human Pose 1. Data acquisition 3. Gesturedetection

  6. Outline • Introduction • Methodology • Data acquisition • Featureextraction • Gesturedetection • Results • Conclusion

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

  8. Featureextraction: Human Pose • Body skeleton • 42-dimensional vector: 14 joints × 3 spatial dimensions • RGB + Depth

  9. Gesturedetection • Dynamic Time Warping (DTW)

  10. Thresholdcomputing • G11 Different samples • Leave-one-outsimilarity measure between different samples and gestures Differentgestures

  11. Outline • Introduction • Methodology • Results • Conclusion

  12. Results

  13. Results

  14. Outline • Introduction • Methodology • Results • Conclusion

  15. Outline • Introduction • Methodology • Results • Conclusion

  16. Conclusion • Methodologyforgesturesegmentation and recognition at thesame time. • Firstresultsindicatetheobjectives are feasible. • Futurework: • Automaticcallibration • Featureweighting (bodyjoints)

  17. ThankYou! ADHD indicatorsmodellingbasedonDynamic Time Warpingfrom RGB data: A feasibilitystudy Antonio Hernández-Vela, Miguel Reyes, Laura Igual, Josep Moya, Verónica Violant, and Sergio Escalera Questions?

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