Child-Robot Interaction in a Musical Dance Game: An Exploratory Comparison Study between Typically Developing Children and Children with Autism

Barnes, Jaclyn; Park, Chung Hyuk; Howard, Ayanna M.; Jeon, Myounghoon

doi:10.1080/10447318.2020.1819667

Cited by 28 publications

(31 citation statements)

References 43 publications

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“…Implicit + categorical: 37 [ 35 , 36 , 38 , 43 - 46 , 48 - 50 , 52 , 55 - 57 , 60 , 64 - 66 , 70 , 74 - 77 , 79 , 81 - 84 , 87 , 89 - 91 , 97 , 101 , 104 - 106 ]…”

Section: Resultsunclassified

Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review

Kaelin¹,

Valizadeh²,

Salgado³

et al. 2021

J Med Internet Res

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Background In the last decade, there has been a rapid increase in research on the use of artificial intelligence (AI) to improve child and youth participation in daily life activities, which is a key rehabilitation outcome. However, existing reviews place variable focus on participation, are narrow in scope, and are restricted to select diagnoses, hindering interpretability regarding the existing scope of AI applications that target the participation of children and youth in a pediatric rehabilitation setting. Objective The aim of this scoping review is to examine how AI is integrated into pediatric rehabilitation interventions targeting the participation of children and youth with disabilities or other diagnosed health conditions in valued activities. Methods We conducted a comprehensive literature search using established Applied Health Sciences and Computer Science databases. Two independent researchers screened and selected the studies based on a systematic procedure. Inclusion criteria were as follows: participation was an explicit study aim or outcome or the targeted focus of the AI application; AI was applied as part of the provided and tested intervention; children or youth with a disability or other diagnosed health conditions were the focus of either the study or AI application or both; and the study was published in English. Data were mapped according to the types of AI, the mode of delivery, the type of personalization, and whether the intervention addressed individual goal-setting. Results The literature search identified 3029 documents, of which 94 met the inclusion criteria. Most of the included studies used multiple applications of AI with the highest prevalence of robotics (72/94, 77%) and human-machine interaction (51/94, 54%). Regarding mode of delivery, most of the included studies described an intervention delivered in-person (84/94, 89%), and only 11% (10/94) were delivered remotely. Most interventions were tailored to groups of individuals (93/94, 99%). Only 1% (1/94) of interventions was tailored to patients’ individually reported participation needs, and only one intervention (1/94, 1%) described individual goal-setting as part of their therapy process or intervention planning. Conclusions There is an increasing amount of research on interventions using AI to target the participation of children and youth with disabilities or other diagnosed health conditions, supporting the potential of using AI in pediatric rehabilitation. On the basis of our results, 3 major gaps for further research and development were identified: a lack of remotely delivered participation-focused interventions using AI; a lack of individual goal-setting integrated in interventions; and a lack of interventions tailored to individually reported participation needs of children, youth, or families.

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“…Implicit + categorical: 37 [ 35 , 36 , 38 , 43 - 46 , 48 - 50 , 52 , 55 - 57 , 60 , 64 - 66 , 70 , 74 - 77 , 79 , 81 - 84 , 87 , 89 - 91 , 97 , 101 , 104 - 106 ]…”

Section: Resultsunclassified

Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review

Kaelin¹,

Valizadeh²,

Salgado³

et al. 2021

J Med Internet Res

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“…Although it is traditionally measured via self-reports, our proof-of-concept study attempts to overcome the shortcomings of this approach by developing machine learning models that can reliably predict its occurrence using EEG measures that directly and objectively capture neural activity. Such models will be crucial to the development of applications that can mitigate the negative effects of mind wandering in real-time [ 18 ], as well as future research that will no longer need to rely on constant task interruptions to determine when mind wandering occurs using thought sampling.…”

Section: Discussionmentioning

confidence: 99%

“…Thought sampling may also change the nature of the task itself since it requires constant interruptions. Unobtrusive detection of mind wandering using machine learning methods thus offers a potential solution that overcomes these challenges and provides avenues for applications that can address the negative impacts of mind wandering in real-time [ 18 ]. Establishing the validation and effectiveness of machine learning in detecting mind wandering across contexts has the potential to eventually replace the need for thought sampling to determine the occurrence of mind wandering.…”

Section: Introductionmentioning

confidence: 99%

Detection of mind wandering using EEG: Within and across individuals

et al. 2021

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Mind wandering is often characterized by attention oriented away from an external task towards our internal, self-generated thoughts. This universal phenomenon has been linked to numerous disruptive functional outcomes, including performance errors and negative affect. Despite its prevalence and impact, studies to date have yet to identify robust behavioral signatures, making unobtrusive, yet reliable detection of mind wandering a difficult but important task for future applications. Here we examined whether electrophysiological measures can be used in machine learning models to accurately predict mind wandering states. We recorded scalp EEG from participants as they performed an auditory target detection task and self-reported whether they were on task or mind wandering. We successfully classified attention states both within (person-dependent) and across (person-independent) individuals using event-related potential (ERP) measures. Non-linear and linear machine learning models detected mind wandering above-chance within subjects: support vector machine (AUC = 0.715) and logistic regression (AUC = 0.635). Importantly, these models also generalized across subjects: support vector machine (AUC = 0.613) and logistic regression (AUC = 0.609), suggesting we can reliably predict a given individual’s attention state based on ERP patterns observed in the group. This study is the first to demonstrate that machine learning models can generalize to “never-seen-before” individuals using electrophysiological measures, highlighting their potential for real-time prediction of covert attention states.

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“…Data collected by the authors can be used in further study on the subject using machine learning or artificial intelligence. Results can be helpful in further clinical trials or be a basis for new therapeutical methods, especially for the people on the autism spectrum [ 65 ]. Modern models of robots introduce new possibilities in development of personalized autonomic systems targeted at people with non-typical cognitive, affective, social, and emotional needs [ 66 , 67 ].…”

Section: Future Research Directionsmentioning

confidence: 99%

Robot-Assisted Autism Therapy (RAAT). Criteria and Types of Experiments Using Anthropomorphic and Zoomorphic Robots. Review of the Research

Szymona¹,

Maciejewski

Karpiński

et al. 2021

Sensors

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Supporting the development of a child with autism is a multi-profile therapeutic work on disturbed areas, especially understanding and linguistic expression used in social communication and development of social contacts. Previous studies show that it is possible to perform some therapy using a robot. This article is a synthesis review of the literature on research with the use of robots in the therapy of children with the diagnosis of early childhood autism. The review includes scientific journals from 2005–2021. Using descriptors: ASD (Autism Spectrum Disorders), Social robots, and Robot-based interventions, an analysis of available research in PubMed, Scopus and Web of Science was done. The results showed that a robot seems to be a great tool that encourages contact and involvement in joint activities. The review of the literature indicates the potential value of the use of robots in the therapy of people with autism as a facilitator in social contacts. Robot-Assisted Autism Therapy (RAAT) can encourage child to talk or do exercises. In the second aspect (prompting during a conversation), a robot encourages eye contact and suggests possible answers, e.g., during free conversation with a peer. In the third aspect (teaching, entertainment), the robot could play with autistic children in games supporting the development of joint attention. These types of games stimulate the development of motor skills and orientation in the body schema. In future work, a validation test would be desirable to check whether children with ASD are able to do the same with a real person by learning distrust and cheating the robot.

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Child-Robot Interaction in a Musical Dance Game: An Exploratory Comparison Study between Typically Developing Children and Children with Autism

Cited by 28 publications

References 43 publications

Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review

Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review

Detection of mind wandering using EEG: Within and across individuals

Robot-Assisted Autism Therapy (RAAT). Criteria and Types of Experiments Using Anthropomorphic and Zoomorphic Robots. Review of the Research

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