Developing AI-Driven Tools and Technologies to Enhance the Lives of Individuals with Autism: Review
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 4 |
| Year of Publication : 2024 |
| Authors : Awatef Balobaid, Noha Mostafa Said |
10.14445/23488379/IJEEE-V11I4P116
How to Cite?
Awatef Balobaid, Noha Mostafa Said, "Developing AI-Driven Tools and Technologies to Enhance the Lives of Individuals with Autism: Review," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 4, pp. 151-158, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I4P116
Abstract:
A developmental disease that impacts behavior, social interaction, and communication is known as Autism Spectrum Disorder (ASD). Over the years, researchers and technologists have strived to develop innovative solutions to support individuals with ASD. Early detection of ASD traits is crucial for timely intervention and management. However, existing conventional ASD screening methods often rely on lengthy questionnaires and domain expert rules, leading to subjectivity and inefficiency and the emergence of Artificial Intelligence (AI) has opened up countless possibilities. This AI advancement has the potential to revolutionize ASD diagnosis processes, benefiting medical clinics and diagnosticians. The aim is to create accessible and efficient tools that enhance the lives of individuals with autism. This review paper explores the impact of AI-driven tools and technologies on enhancing the lives of individuals with autism. Specifically, highlight the challenges and future directions in this field.
Keywords:
Autism Spectrum Disorder (ASD), AI-driven tools, Technologies, Individuals, Developing, Machine intelligence.
References:
[1] Hye Ran Park et al., “A Short Review on the Current Understanding of Autism Spectrum Disorders,” Experimental Neurobiology, vol. 25, no. 1, pp. 1-13, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Christine Ecker, Susan Y. Bookheimer, and Declan G.M. Murphy, “Neuroimaging in Autism Spectrum Disorder: Brain Structure and Function across the Lifespan,” The Lancet Neurology, vol. 14, no. 11, pp. 1121-1134, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Pranav Gupta et al., “Fostering Collective Intelligence in Human-AI Collaboration: Laying the Groundwork for COHUMAIN,” Topics in Cognitive Science, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Hasan Alkahtani, Theyazn H.H. Aldhyani, and Mohammed Y. Alzahrani, “Deep Learning Algorithms to Identify Autism Spectrum Disorder in Children-Based Facial Landmarks,” Applied Sciences, vol. 13, no. 8, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Seyed Reza Shahamiri, and Fadi Thabtah, “Autism AI: A New Autism Screening System Based on Artificial Intelligence,” Cognitive Computation, vol. 12, pp. 766-777, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Eman Helmy et al., “Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey,” Biomedicines, vol. 11, no. 7, pp. 1-36, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Sergio Rubio-Martín et al., “Early Detection of Autism Spectrum Disorder through AI-Powered Analysis of Social Media Texts,” 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L’Aquila, Italy, pp. 235-240, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mahmoud Elbattah et al., “Applications of Machine Learning Methods to Assist the Diagnosis of Autism Spectrum Disorder,” Neural Engineering Techniques for Autism Spectrum Disorder,” vol. 2, pp. 99-119, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Nur Anis Suhaila, and Norazah Mohd Nordin, “Assistive Technology for Autism Spectrum Disorder: Systematic Literature Review,” International Journal of Advanced Research in Education and Society, vol. 4, no. 2, pp. 25-39, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Lukas Wohofsky et al., “Assistive Technology to Support People with Autism Spectrum Disorder in their Autonomy and Safety: A Scoping Review,” Technology and Disability, vol. 34, no. 1, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Deepak Giri, and Erin Brady, “Exploring Outlooks towards Generative AI-Based Assistive Technologies for People with Autism,” arXiv, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Yarlagadda Bhargavi, Bini D., and Shajin Prince, “AI-Based Emotion Therapy Bot for Children with Autism Spectrum Disorder (ASD),” 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, pp. 1895-1899, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yirong Li, Ting Wang, and Qiuxin Wang, “Application of Artificial Intelligence in Intervention of Autistic Children,” International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID), vol. 12168, pp. 305-309, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Anfeng Xu et al., “Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings,” arXiv, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Celeste Mason, and Frank Steinicke, “Personalization of Intelligent Virtual Agents for Motion Training in Social Settings,” 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Christchurch, New Zealand, pp. 319-322, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Panagiotis Kourtesis et al., “Virtual Reality Training of Social Skills in Autism Spectrum Disorder: An Examination of Acceptability, Usability, User Experience, Social Skills, and Executive Functions,” arXiv, pp. 1-37, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Keith C. Radley et al., “Building Social Skills: An Investigation of a LEGO-Centered Social Skills Intervention,” Advances in Neurodevelopmental Disorders, vol. 4, pp. 134-145, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] David J. Cox, and Adrienne M. Jennings, “The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services,” Behavior Analysis in Practice, vol. 17, pp. 123-136, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Xin Sun, “Conversational Interface Cooperating with AI and Monitoring Technology Adopting Human-in-the-Loop Interaction for Intelligent Behavioral Intervention,” Companion Proceedings of the 28th International Conference on Intelligent User Interfaces, pp. 243- 245, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ashley Sineath et al., “Monitoring Intervention Fidelity of a Lifestyle Behavioral Intervention Delivered through Telehealth,” mhealth, vol. 3, pp. 1-12, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] M. Avetisyan, “Sensory Integration, Description Features and the Treatment,” Main Issues of Pedagogy and Psychology, vol. 8, no. 1, pp. 38-44, 2021.
[Google Scholar]
[22] Krzysztof Szczurowski, and Matt Smith, “Challenges of Experimenting with Virtual Reality,” 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Christchurch, New Zealand, pp. 108-113, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Roxana Rebolledo Font de la Vall, and Fabián González Araya, “Exploring the Benefits and Challenges of AI-Language Learning Tools,” International Journal of Social Sciences and Humanities Invention, vol. 10, no. 1, pp. 7569-7576, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Amina Abubakar, and Patricia Kipkemoi, “Early Intervention in Autism Spectrum Disorder: The Need for an International Approach,” Developmental Medicine & Child Neurology, vol. 64, no. 9, pp. 1051-1058, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Drew Grant et al., “Considerations and Challenges for Real-World Deployment of an Acoustic-Based COVID-19 Screening System,” Sensors, vol. 22, no. 23, pp. 1-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Cinzia Daraio, and Wolfgang Glänzel, “Grand Challenges in Data Integration State of the Art and Future Perspectives: An Introduction,” Scientometrics, vol. 108, pp. 391-400, 2016.
[CrossRef] [Google Scholar] [Publisher Link]