adhd indicators modelling based on dynamic time warping from rgb data: a feasibility study antonio...
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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
Inattention
Hyperactivity
Impulsivity
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Outline
1. Introduction
2. Methodology
3. Results
4. Conclusion
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Introduction
• Video-based behavior analysis for ADHD diagnosis in children between 8-11 years.
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Introduction
• Behavior analysis Human pose information along time
HeadBodyHands
time Gestures
Inattention
Hyperactivity
Impulsivity
1. Data acquisition2. Feature extraction: Human Pose3. Gesture detection
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Outline
1. Introduction
2. Methodology
1. Data acquisition
2. Feature extraction
3. Gesture detection
3. Results
4. Conclusion
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Data aqcuisition
Microsoft’s Kinect
RGB + Depth
• Invariant to color, texture and lighting conditions• Human pose directly obtained
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Feature extraction: Human Pose
RGB + Depth Body skeleton
• 42-dimensional vector: 14 joints × 3 spatial dimensions
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Gesture detection
• Dynamic Time Warping (DTW)
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Threshold computing
• Leave-one-out similarity measure between different samples and gestures
G1G11
G12
…
G13
G2G21
G22
…
G23
… GnGn1
Gn2
Gn3
G11
Different gestures
Differentsamples
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Outline
1. Introduction
2. Methodology
3. Results
4. Conclusion
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Results
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Results
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Outline
1. Introduction
2. Methodology
3. Results
4. Conclusion
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Outline
1. Introduction
2. Methodology
3. Results
4. Conclusion
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Conclusion
• 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)
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?