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Deep Learning Algorithms for Behavioral Analysis in Diagnosing Neurodevelopmental Disorders

Author

Listed:
  • Hasan Alkahtani

    (King Salman Center for Disability Research, P.O. Box 94682, Riyadh 11614, Saudi Arabia
    Computer Science Department, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Zeyad A. T. Ahmed

    (Department of Computer Science, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India)

  • Theyazn H. H. Aldhyani

    (King Salman Center for Disability Research, P.O. Box 94682, Riyadh 11614, Saudi Arabia
    Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Mukti E. Jadhav

    (Department of Computer Sciences, Shri Shivaji Science and Arts College, Chikhli Dist Buldana 443201, India)

  • Ahmed Abdullah Alqarni

    (King Salman Center for Disability Research, P.O. Box 94682, Riyadh 11614, Saudi Arabia
    Department of Computer Sciences and Information Technology, Al Baha University, P.O. Box 1988, Al Baha 65431, Saudi Arabia)

Abstract

Autism spectrum disorder (ASD), or autism, can be diagnosed based on a lack of behavioral skills and social communication. The most prominent method of diagnosing ASD in children is observing the child’s behavior, including some of the signs that the child repeats. Hand flapping is a common stimming behavior in children with ASD. This research paper aims to identify children’s abnormal behavior, which might be a sign of autism, using videos recorded in a natural setting during the children’s regular activities. Specifically, this study seeks to classify self-stimulatory activities, such as hand flapping, as well as normal behavior in real-time. Two deep learning video classification methods are used to be trained on the publicly available Self-Stimulatory Behavior Dataset (SSBD). The first method is VGG-16-LSTM; VGG-16 to spatial feature extraction and long short-term memory networks (LSTM) for temporal features. The second method is a long-term recurrent convolutional network (LRCN) that learns spatial and temporal features immediately in end-to-end training. The VGG-16-LSTM achieved 0.93% on the testing set, while the LRCN model achieved an accuracy of 0.96% on the testing set.

Suggested Citation

  • Hasan Alkahtani & Zeyad A. T. Ahmed & Theyazn H. H. Aldhyani & Mukti E. Jadhav & Ahmed Abdullah Alqarni, 2023. "Deep Learning Algorithms for Behavioral Analysis in Diagnosing Neurodevelopmental Disorders," Mathematics, MDPI, vol. 11(19), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4208-:d:1255835
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    References listed on IDEAS

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    1. Yi Wang & Zhengxiang He & Liguan Wang, 2021. "Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines," Mathematics, MDPI, vol. 9(22), pages 1-14, November.
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    Cited by:

    1. Zeyad A. T. Ahmed & Eid Albalawi & Theyazn H. H. Aldhyani & Mukti E. Jadhav & Prachi Janrao & Mansour Ratib Mohammad Obeidat, 2023. "Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders," Data, MDPI, vol. 8(11), pages 1-27, November.
    2. Hasan Alkahtani & Theyazn H. H. Aldhyani & Zeyad A. T. Ahmed & Ahmed Abdullah Alqarni, 2023. "Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder," Mathematics, MDPI, vol. 11(22), pages 1-31, November.

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