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Modeling ADHD Indicators Based on Dynamic Time Warping from RGB Data: A Feasibility Study

This feasibility study explores the automatic detection of ADHD indicators in children aged 8-11 through video-based behavior analysis. Utilizing Microsoft Kinect for data acquisition, the research employs Dynamic Time Warping (DTW) to analyze human pose data and identify behavioral patterns associated with inattention, hyperactivity, and impulsivity. The methodology encompasses feature extraction, gesture detection, and results indicate the potential for effective ADHD diagnosis. Future work aims to improve calibration and feature weighting for enhanced accuracy.

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Modeling ADHD Indicators 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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