SENSES-ASD: a social-emotional nurturing and skill enhancement system for autism spectrum disorder

Haya Abu-Nowar; Adeeb Sait; Tawfik Al-Hadhrami; Mohammed Al-Sarem; Sultan Noman Qasem

doi:10.7717/peerj-cs.1792

SENSES-ASD: a social-emotional nurturing and skill enhancement system for autism spectrum disorder

PeerJ Comput Sci. 2024 Feb 8:10:e1792. doi: 10.7717/peerj-cs.1792. eCollection 2024.

Authors

Haya Abu-Nowar^#¹, Adeeb Sait^#¹, Tawfik Al-Hadhrami¹, Mohammed Al-Sarem², Sultan Noman Qasem³

Affiliations

¹ Computer Science Department, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom.
² College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
³ Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

^# Contributed equally.

Abstract

This article introduces the Social-Emotional Nurturing and Skill Enhancement System (SENSES-ASD) as an innovative method for assisting individuals with autism spectrum disorder (ASD). Leveraging deep learning technologies, specifically convolutional neural networks (CNN), our approach promotes facial emotion recognition, enhancing social interactions and communication. The methodology involves the use of the Xception CNN model trained on the FER-2013 dataset. The designed system accepts a variety of media inputs, successfully classifying and predicting seven primary emotional states. Results show that our system achieved a peak accuracy rate of 71% on the training dataset and 66% on the validation dataset. The novelty of our work lies in the intricate combination of deep learning methods specifically tailored for high-functioning autistic adults and the development of a user interface that caters to their unique cognitive and sensory sensitivities. This offers a novel perspective on utilising technological advances for ASD intervention, especially in the domain of emotion recognition.

Keywords: Autism spectrum disorder; Convolutional neural network; Deep learning; Emotion recognition.

Grants and funding

This work is funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) through Research Partnership Program no RP-21-07-09. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.