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Deep Learning-Based Action Classification for Parents and Children with Down Syndrome in Educational Settings.

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Title: Deep Learning-Based Action Classification for Parents and Children with Down Syndrome in Educational Settings.
Authors: Galindo-Lopez, Carlos Ramon (AUTHOR), Beltran, Jessica (AUTHOR), Perez, Cynthia B. (AUTHOR), Caro, Karina (AUTHOR), Macias, Adrian (AUTHOR), Castro, Luis A. (AUTHOR)
Source: International Journal of Human-Computer Interaction. Sep2025, Vol. 41 Issue 18, p11528-11542. 15p.
Subjects: Deep learning, Down syndrome, Convolutional neural networks, Assistive technology, Human activity recognition, Behavioral assessment, Parent-child relationships
Abstract: Human activity recognition (HAR) has become a key technology for improving the quality of life for individuals with special needs, such as older adults and children with Down Syndrome (CwDS). This study presents a novel application of HAR systems to detect directive behaviors in parent-child interactions, focusing on parents of CwDS during educational activities. Using video data, we developed two models: a Convolutional Neural Network (CNN) and a hybrid CNN-LSTM model, to recognize subtle cues such as physical proximity and verbal interactions. The CNN3D model achieved over 90% accuracy in detecting approach behaviors and around 65% for verbal expressions. The CNN-LSTM model outperformed CNN3D in classifying verbal expressions, achieving over 68% accuracy. These results highlight the potential of deep learning classifiers for analyzing subtle parent-child interactions, offering valuable insights into parent-child dynamics and contributing to the development of assistive tools for studying educational settings. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Deep Learning-Based Action Classification for Parents and Children with Down Syndrome in Educational Settings.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Sep2025, Vol. 41 Issue 18, p11528-11542. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Down+syndrome%22">Down syndrome</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Assistive+technology%22">Assistive technology</searchLink><br /><searchLink fieldCode="DE" term="%22Human+activity+recognition%22">Human activity recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Parent-child+relationships%22">Parent-child relationships</searchLink>
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  Data: Human activity recognition (HAR) has become a key technology for improving the quality of life for individuals with special needs, such as older adults and children with Down Syndrome (CwDS). This study presents a novel application of HAR systems to detect directive behaviors in parent-child interactions, focusing on parents of CwDS during educational activities. Using video data, we developed two models: a Convolutional Neural Network (CNN) and a hybrid CNN-LSTM model, to recognize subtle cues such as physical proximity and verbal interactions. The CNN3D model achieved over 90% accuracy in detecting approach behaviors and around 65% for verbal expressions. The CNN-LSTM model outperformed CNN3D in classifying verbal expressions, achieving over 68% accuracy. These results highlight the potential of deep learning classifiers for analyzing subtle parent-child interactions, offering valuable insights into parent-child dynamics and contributing to the development of assistive tools for studying educational settings. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/10447318.2024.2443269
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      – Code: eng
        Text: English
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Down syndrome
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Assistive technology
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      – SubjectFull: Human activity recognition
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      – SubjectFull: Behavioral assessment
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      – SubjectFull: Parent-child relationships
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      – TitleFull: Deep Learning-Based Action Classification for Parents and Children with Down Syndrome in Educational Settings.
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            NameFull: Beltran, Jessica
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              M: 09
              Text: Sep2025
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              Y: 2025
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