Deep Learning Algorithms for Behavioral Analysis in Diagnosing Neurodevelopmental Disorders

Hasan Alkahtani, Zeyad A. T. Ahmed, Theyazn H. H. Aldhyani (), Mukti E. Jadhav and Ahmed Abdullah Alqarni
Additional contact information
Hasan Alkahtani: King Salman Center for Disability Research, P.O. Box 94682, Riyadh 11614, 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
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

Mathematics, 2023, vol. 11, issue 19, 1-18

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.

Keywords: autism spectrum disorder; deep learning; computer vision; behavior analysis; hand flapping; human action recognition; video classification; long short-term memory networks; convolutional neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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