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Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural

Vinícius Silva1, Filomena Soares1, Celina P Leão1

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Summary
This summary is machine-generated.

Researchers developed a method to detect typical and stereotypical actions in children with Autism Spectrum Disorder (ASD) using joint coordinate data. The sequence of joints significantly impacts model performance, achieving 92.4% accuracy in classifying behaviors.

Keywords:
autism spectrum disorderconvolutional neural networkhuman action recognitionhuman computer interaction

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Developmental Psychology
  • Computer Vision

Background:

  • Children with Autism Spectrum Disorder (ASD) often struggle with social interaction.
  • Technological tools, including social robots, are being developed to support children with ASD.
  • Current social robots lack the ability to adapt their behavior due to a lack of user action recognition.

Purpose of the Study:

  • To propose a method for real-time detection of typical and stereotypical actions in children with ASD.
  • To investigate the impact of joint sequence representation on the performance of action recognition models.
  • To develop an accurate classification system for behavioral patterns in children with ASD.

Main Methods:

  • Utilized Intel RealSense and Nuitrack SDK to extract user joint coordinates.
  • Mapped temporal and spatial joint dynamics onto a color image-based representation.
  • Conducted experiments varying joint order in sequences and analyzed using statistical methods.
  • Trained a Convolutional Neural Network (CNN) for behavior classification.

Main Results:

  • Found statistically significant differences in model performance based on the sequence of joints.
  • The developed CNN model achieved a mean accuracy of 92.4% in classifying typical and stereotypical actions.
  • The entire detection and classification pipeline operated at an average of 31 FPS.

Conclusions:

  • The order of joint representation is a critical factor influencing the performance of action recognition models for children with ASD.
  • The proposed method offers an effective approach for real-time behavioral analysis in children with ASD.
  • This technology can enhance the adaptive capabilities of social robots and support systems for individuals with ASD.