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Kim W, Seong M, Kim KJ, Kim S. Engagnition: A multi-dimensional dataset for engagement recognition of children with autism spectrum disorder. Sci Data 2024; 11:299. [PMID: 38491000 PMCID: PMC10942992 DOI: 10.1038/s41597-024-03132-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
Engagement plays a key role in improving the cognitive and motor development of children with autism spectrum disorder (ASD). Sensing and recognizing their engagement is crucial before sustaining and improving the engagement. Engaging technologies involving interactive and multi-sensory stimuli have improved engagement and alleviated hyperactive and stereotyped behaviors. However, due to the scarcity of data on engagement recognition for children with ASD, limited access to and small pools of participants, and the prohibitive application requirements such as robots, high cost, and expertise, implementation in real world is challenging. However, serious games have the potential to overcome those drawbacks and are suitable for practical use in the field. This study proposes Engagnition, a dataset for engagement recognition of children with ASD (N = 57) using a serious game, "Defeat the Monster," based on enhancing recognition and classification skills. The dataset consists of physiological and behavioral responses, annotated by experts. For technical validation, we report the distributions of engagement and intervention, and the signal-to-noise ratio of physiological signals.
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Affiliation(s)
- Won Kim
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Minwoo Seong
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Kyung-Joong Kim
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - SeungJun Kim
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea.
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Jennings AM, Cox DJ. Starting the Conversation Around the Ethical Use of Artificial Intelligence in Applied Behavior Analysis. Behav Anal Pract 2024; 17:107-122. [PMID: 38405299 PMCID: PMC10891004 DOI: 10.1007/s40617-023-00868-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/02/2023] [Indexed: 02/27/2024] Open
Abstract
Artificial intelligence (AI) is increasingly a part of our everyday lives. Though much AI work in healthcare has been outside of applied behavior analysis (ABA), researchers within ABA have begun to demonstrate many different ways that AI might improve the delivery of ABA services. Though AI offers many exciting advances, absent from the behavior analytic literature thus far is conversation around ethical considerations when developing, building, and deploying AI technologies. Further, though AI is already in the process of coming to ABA, it is unknown the extent to which behavior analytic practitioners are familiar (and comfortable) with the use of AI in ABA. The purpose of this article is twofold. First, to describe how existing ethical publications (e.g., BACB Code of Ethics) do and do not speak to the unique ethical concerns with deploying AI in everyday, ABA service delivery settings. Second, to raise questions for consideration that might inform future ethical guidelines when developing and using AI in ABA service delivery. In total, we hope this article sparks proactive dialog around the ethical use of AI in ABA before the field is required to have a reactionary conversation.
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Affiliation(s)
- Adrienne M. Jennings
- Department of Behavioral Science, Daemen University, 4380 Main Street, Amherst, NY 14226 USA
| | - David J. Cox
- Institute for Applied Behavioral Science, Endicott College, Beverly, MA USA
- RethinkFirst, 49 W 27th St, 8th floor, New York, NY 10001 USA
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Cox DJ, Jennings AM. The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services. Behav Anal Pract 2024; 17:123-136. [PMID: 38405282 PMCID: PMC10890993 DOI: 10.1007/s40617-023-00864-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2023] [Indexed: 02/27/2024] Open
Abstract
Artificial intelligence (AI) has begun to affect nearly every aspect of our daily lives and nearly every industry and profession. Many readers of this journal likely work in one or more areas of behavioral health. For readers who work in behavioral health and who are interested in AI, the purpose of this article is to highlight the pervasiveness of AI research being conducted around many facets of behavioral health service delivery. To do this, we first provide a brief overview of some of the areas within AI and the types of problems each area of AI attempts to solve. We then outline the prototypical client journey in behavioral healthcare beginning with diagnosis/assessment and ending with intervention withdrawal or ongoing monitoring. Next, for each stage in the client journey, we highlight several areas that parallel existing behavior analytic practice where researchers have begun to use AI, often to improve the efficiency of service delivery or to learn new things that improve the effectiveness of behavioral health services. Finally, for those whose appetite has been whet for getting involved with AI, we close by describing three roles they might consider trying out and that parallel the three main domains of behavior analysis. These three roles are an AI tool designer (akin to EAB), AI tool implementer (akin to ABA), or AI tool supporter (akin to practice).
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Affiliation(s)
- David J. Cox
- Department of Applied Behavior Analysis, Endicott College, Beverly, MA USA
| | - Adrienne M. Jennings
- Department of Behavioral Science, Daemen University, 4380 Main Street, Amherst, NY USA
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Ferina J, Kruger M, Kruger U, Ryan D, Anderson C, Foster J, Hamlin T, Hahn J. Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data. J Pers Med 2023; 13:1513. [PMID: 37888124 PMCID: PMC10608042 DOI: 10.3390/jpm13101513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
Autism spectrum disorder (ASD), characterized by social, communication, and behavioral abnormalities, affects 1 in 36 children according to the CDC. Several co-occurring conditions are often associated with ASD, including sleep and immune disorders and gastrointestinal (GI) problems. ASD is also associated with sensory sensitivities. Some individuals with ASD exhibit episodes of challenging behaviors that can endanger themselves or others, including aggression and self-injurious behavior (SIB). In this work, we explored the use of artificial intelligence models to predict behavior episodes based on past data of co-occurring conditions and environmental factors for 80 individuals in a residential setting. We found that our models predict occurrences of behavior and non-behavior with accuracies as high as 90% for some individuals, and that environmental, as well as gastrointestinal, factors are notable predictors across the population examined. While more work is needed to examine the underlying connections between the factors and the behaviors, having reasonably accurate predictions for behaviors has the potential to improve the quality of life of some individuals with ASD.
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Affiliation(s)
- Jennifer Ferina
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
| | - Melanie Kruger
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
- Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Uwe Kruger
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
| | - Daniel Ryan
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Conor Anderson
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Jenny Foster
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Theresa Hamlin
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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Farooq MS, Tehseen R, Sabir M, Atal Z. Detection of autism spectrum disorder (ASD) in children and adults using machine learning. Sci Rep 2023; 13:9605. [PMID: 37311766 DOI: 10.1038/s41598-023-35910-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 05/25/2023] [Indexed: 06/15/2023] Open
Abstract
Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).
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Affiliation(s)
- Muhammad Shoaib Farooq
- Department of Artificial Intelligence, University of Management and Technology, Lahore, 54000, Pakistan
| | - Rabia Tehseen
- Department of Computer Science, University of Central Punjab, Lahore, 54000, Pakistan
| | - Maidah Sabir
- Department of Artificial Intelligence, University of Management and Technology, Lahore, 54000, Pakistan
| | - Zabihullah Atal
- Department of Computer Science, Kardan University, Kabul, 1007, Afghanistan.
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deLeyer‐Tiarks JM, Li MG, Levine‐Schmitt M, Andrade B, Bray MA, Peters E. Advancing autism technology. PSYCHOLOGY IN THE SCHOOLS 2022. [DOI: 10.1002/pits.22802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Michael G. Li
- Department of Educational Psychology, Neag School of Education University of Connecticut Storrs Connecticut USA
| | - Michelle Levine‐Schmitt
- Department of Educational Psychology, Neag School of Education University of Connecticut Storrs Connecticut USA
| | - Bryndis Andrade
- Department of Educational Psychology, Neag School of Education University of Connecticut Storrs Connecticut USA
| | - Melissa A. Bray
- Department of Educational Psychology, Neag School of Education University of Connecticut Storrs Connecticut USA
| | - Emily Peters
- Department of Educational Psychology, Neag School of Education University of Connecticut Storrs Connecticut USA
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Internet of Things (IoT)-Enhanced Applied Behavior Analysis (ABA) for Special Education Needs. SENSORS 2021; 21:s21196693. [PMID: 34641011 PMCID: PMC8513056 DOI: 10.3390/s21196693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/21/2021] [Accepted: 10/05/2021] [Indexed: 11/16/2022]
Abstract
Applied behavior analysis (ABA) has become a popular behavioral therapy in the special education needs (SEN) community. ABA is used to manage SEN students’ behaviors by solving problems in socially important settings, and puts emphasis on having precise measurements on physical and observable events. In this work, we present how Internet of Things (IoT) technologies can be applied to enhance ABA therapy in normal SEN classroom settings. We measured (1) learning performance data, (2) learners’ physiological data, and (3) learning environment sensors’ data. Upon preliminary analysis, we have found that learners’ physiological data is highly diverse, while learner performance seems to be related to learners’ electrodermal activity. Our preliminary findings suggest the possibility of enhancing ABA for SEN with IoT technologies.
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Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00447-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractThe number of autism spectrum disorder individuals is dramatically increasing. For them, it is difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which may be the cause of social isolation and economic loss for all their family. This SLR aims at understanding and summarizing the current research work on this topic and analyze the limitations and open challenges to address future work. We consider papers published between 2015 and the beginning of 2021. The initial selection included about 2140 papers. 11 of them respected our selection criteria. The papers have been analyzed by mainly considering: (1) the kind of action taken on the autistic individual, (2) the considered wearables, (3) the machine learning approaches, and (4) the evaluation strategies. Results revealed that the topic is very relevant, but there are many limitations in the considered studies, such as reduced number of participants, absence of datasets and experimentation in real contexts, need for considering privacy issues, and the adoption of appropriate validation approaches. The issues highlighted in this analysis may be useful for improving machine learning techniques and highlighting areas of interest in which experimenting with the use of different noninvasive sensors.
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Zheng ZK, Staubitz JE, Weitlauf AS, Staubitz J, Pollack M, Shibley L, Hopton M, Martin W, Swanson A, Juárez P, Warren ZE, Sarkar N. A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC). SENSORS 2021; 21:s21020370. [PMID: 33430371 PMCID: PMC7826816 DOI: 10.3390/s21020370] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/17/2020] [Accepted: 01/04/2021] [Indexed: 11/16/2022]
Abstract
Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.
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Affiliation(s)
- Zhaobo K. Zheng
- Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37240, USA;
- Correspondence:
| | - John E. Staubitz
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - Amy S. Weitlauf
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
| | - Johanna Staubitz
- Department of Special Education, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.S.); (M.P.)
| | - Marney Pollack
- Department of Special Education, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.S.); (M.P.)
| | - Lauren Shibley
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - Michelle Hopton
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - William Martin
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - Amy Swanson
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
| | - Pablo Juárez
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
- Department of Special Education, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.S.); (M.P.)
| | - Zachary E. Warren
- Treatment and Research Institute of Autism Spectrum Disorders, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.E.S.); (A.S.W.); (L.S.); (M.H.); (W.M.); (A.S.); (P.J.); (Z.E.W.)
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
- Department of Special Education, Vanderbilt University Medical Center, Nashville, TN 37240, USA; (J.S.); (M.P.)
| | - Nilanjan Sarkar
- Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37240, USA;
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37240, USA
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