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

4

Introduction

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

• Automatic detection of ADHD visual indicators

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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

15

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?