AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

Autism spectrum disorder is a developmental disorder characterized by significant social, communication, and behavioral challenges. Individuals diagnosed with autism, intellectual, and developmental disabilities (AUIDD) typically require long-term care and targeted treatment and teaching. Effective treatment of AUIDD relies on efficient and careful behavioral observations done by trained applied behavioral analysts (ABAs). However, this process overburdens ABAs by requiring the clinicians to collect and analyze data, identify the problem behaviors, conduct pattern analysis to categorize and predict categorical outcomes, hypothesize responsiveness to treatments, and detect the effects of treatment plans. Successful integration of digital technologies into clinical decision-making pipelines and the advancements in automated decision making using artificial intelligence (AI) algorithms highlights the importance of augmenting teaching and treatments using novel algorithms and high-fidelity sensors. In this article, we present an AI-augmented learning and applied behavior analytics (AI-ABA) platform to provide personalized treatment and learning plans to AUIDD individuals. By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior using reinforcement-based augmented or virtual reality and other mobile platforms. Thus, AI-ABA could assist clinicians to focus on making precise data-driven decisions and increase the quality of individualized interventions for individuals with AUIDD.

[1]  Natalia Kucirkova.  iPads in early years, separating assumptions and evidence , 2014 .

[2]  Benjamin Zablotsky,et al.  Prevalence of Children Aged 3-17 Years With Developmental Disabilities, by Urbanicity: United States, 2015-2018. , 2020, National health statistics reports.

[3]  Akshay Vijayan,et al.  A Framework for Intelligent Learning Assistant Platform Based on Cognitive Computing for Children with Autism Spectrum Disorder , 2018, 2018 International CET Conference on Control, Communication, and Computing (IC4).

[4]  R. Lang,et al.  Quantitative-Analysis of Behavioral Interventions to Treat Sleep Problems in Children with Autism , 2020, Developmental neurorehabilitation.

[5]  Sebastiaan Mathôt,et al.  PyGaze: An open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments , 2014, Behavior research methods.

[6]  Jorge Bacca,et al.  Augmented Reality Trends in Education: A Systematic Review of Research and Applications , 2014, J. Educ. Technol. Soc..

[7]  T. Parsons,et al.  The potential of function-led virtual environments for ecologically valid measures of executive function in experimental and clinical neuropsychology , 2017, Neuropsychological rehabilitation.

[8]  Nogol Memari,et al.  Speech Analysis with Deep Learning to Determine Speech Therapy for Learning Difficulties , 2020 .

[9]  Arshya Vahabzadeh,et al.  Safety and Lack of Negative Effects of Wearable Augmented-Reality Social Communication Aid for Children and Adults with Autism , 2017, bioRxiv.

[10]  Jack M. Balkin,et al.  The Digital Transformation of Education , 2016 .

[11]  D. Wall,et al.  Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism , 2012, PloS one.

[12]  Cesare Furlanello,et al.  Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders , 2017, Signal Process..

[13]  W. Ahearn,et al.  Assessing and treating vocal stereotypy in children with autism. , 2007, Journal of applied behavior analysis.

[14]  U. Raghavendra,et al.  A deep learning approach for Parkinson’s disease diagnosis from EEG signals , 2018, Neural Computing and Applications.

[15]  Tony Ro,et al.  An interactive facial-expression training platform for individuals with autism spectrum disorder , 2016, 2016 IEEE MIT Undergraduate Research Technology Conference (URTC).

[16]  Nilanjan Sarkar,et al.  Hand-in-Hand: A Communication-Enhancement Collaborative Virtual Reality System for Promoting Social Interaction in Children With Autism Spectrum Disorders , 2018, IEEE Transactions on Human-Machine Systems.

[17]  Arun Das,et al.  Stuttering Speech Disfluency Prediction using Explainable Attribution Vectors of Facial Muscle Movements , 2020, ArXiv.

[18]  Lian Loke,et al.  From autism educators to game designers: integrating teaching strategies into game design for autism education support , 2018, OZCHI.

[19]  E. Hong,et al.  Intercontinental Telehealth to Train Japanese Interventionists in Incidental Teaching for Children with Autism , 2020 .

[20]  Alessandro G. Di Nuovo,et al.  Deep Learning Systems for Estimating Visual Attention in Robot-Assisted Therapy of Children with Autism and Intellectual Disability , 2018, Robotics.

[21]  Florian Alt,et al.  TaBooGa: A Hybrid Learning App to Support Children's Reading Motivation , 2017, IDC.

[22]  A. Franco,et al.  NeuroImage: Clinical , 2022 .

[23]  Jing Fan,et al.  Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD) , 2016, 2016 IEEE Virtual Reality (VR).

[24]  Brian Penchina,et al.  Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism , 2020, BI.

[25]  F. Gresham.  Establishing the Technical Adequacy of Functional Behavioral Assessment: Conceptual and Measurement Challenges , 2003 .

[26]  G. B. Schaefer.  Clinical Genetic Aspects of Autism Spectrum Disorders , 2016 .

[27]  Arun Das,et al.  Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey , 2020, ArXiv.

[28]  Edward J. Golob,et al.  Interpretable Self-Supervised Facial Micro-Expression Learning to Predict Cognitive State and Neurological Disorders , 2021, AAAI.

[29]  C. Miguel,et al.  The effects of matched stimulation and response interruption and redirection on vocal stereotypy. , 2012, Journal of applied behavior analysis.

[30]  Louis-Philippe Morency,et al.  OpenFace 2.0: Facial Behavior Analysis Toolkit , 2018, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[31]  Pamela Mathy,et al.  Predictors of Phrase and Fluent Speech in Children With Autism and Severe Language Delay , 2013, Pediatrics.

[32]  2019 International Conference on Data Mining Workshops (ICDMW) , 2019 .

[33]  Erik Linstead,et al.  An Application of Neural Networks to Predicting Mastery of Learning Outcomes in the Treatment of Autism Spectrum Disorder , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[34]  N. Sajaniemi,et al.  Enhancing peer interaction: an aspect of a high-quality learning environment in Finnish early childhood special education , 2017 .

[35]  M. Cetin,et al.  Siblings Attitudes Towards Persons with Severe/Profound and Multiple Disabilities in Turkey , 2015 .

[36]  Shang-Lin Wu,et al.  EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method , 2017, IEEE Systems, Man, and Cybernetics Magazine.

[37]  So Yoon Kim,et al.  The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review , 2019, Soa--ch'ongsonyon chongsin uihak = Journal of child & adolescent psychiatry.

[38]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[39]  O. Healy,et al.  The Use of Differential Reinforcement of Other Behaviours to Establish Inhibitory Stimulus Control for the Management of Vocal Stereotypy in Children with Autism , 2018, Developmental neurorehabilitation.

[40]  Inzamam Ul Haque,et al.  A Facial Expression Recognition Application Development Using Deep Convolutional Neural Network For Children With Autism Spectrum Disorder To Help Identify Human Emotions , 2019 .

[41]  K. Gray,et al.  Interactions of pre-symbolic children with developmental disabilities with their mothers and siblings. , 2015, International journal of language & communication disorders.

[42]  Ronald Azuma,et al.  Recent Advances in Augmented Reality , 2001, IEEE Computer Graphics and Applications.

[43]  Prashant Pandey,et al.  Guided weak supervision for action recognition with scarce data to assess skills of children with autism , 2020, AAAI.

[44]  Wan Ng,et al.  Change and Continuity in Educational Uses of New Digital Technologies , 2015 .

[45]  Mei-Ling Shyu,et al.  Deep Learning Based Multimedia Data Mining for Autism Spectrum Disorder (ASD) Diagnosis , 2019, 2019 International Conference on Data Mining Workshops (ICDMW).

[46]  F. Thabtah.  Machine learning in autistic spectrum disorder behavioral research: A review and ways forward , 2019, Informatics for health & social care.

[47]  John J. Prevost,et al.  Human Action Performance Using Deep Neuro-Fuzzy Recurrent Attention Model , 2020, IEEE Access.

[48]  Jacek Ruminski,et al.  Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters , 2019, IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society.