Engagement Evaluation for Autism Intervention by Robots Based on Dynamic Bayesian Network and Expert Elicitation

Feng, Yongli; Jia, Qingxuan; Chu, Ming; Wei, Wei

doi:10.1109/access.2017.2754291

Cited by 24 publications

(30 citation statements)

References 30 publications

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“…In case of robotics therapy, joint attention [9], ability to imitate [10], verbal communication [11] and social activities [12] are mostly targeted to measure the improvement in ASD. Humanoid robots are gaining more attention for autism therapy as they are controllable, accurate, low cost and adaptive to environment [13], [14]. The current research trends are inclined towards development of novel robotbased therapies due to the inquisition of ASD children in robot [10], [12].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-Robot Therapy for Improving Joint Attention and Imitation of ASD Children

et al. 2019

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Robot-mediated therapies for autism spectrum disorder (ASD) have shown promising results in the past. We have proposed a novel mathematical model based on an adaptive multi-robot therapy of ASD children focusing on two main impairments in autism: 1) joint attention and 2) imitation. Joint attention intervention is based on three different least-to-most (LTM) cues, whereas the adaptive imitation module uses joint attention for activation of the robot. The proposed model uses a multi-robot system as a therapist without any external stimuli (from the environment) to improve the skills of the ASD child. Another novel aspect of this paper is the deployment of a multi-robot system for introducing the ASD child to the concept of multi-person communication. This is particularly useful as, unlike humans, robots can be more consistent and relatively immune to fatigue. Two different therapies of human-robot interaction (i.e., with and without interrobot communication) have been conducted. The model has been tested on 12 ASD children, eight sessions for each intervention over a period of six months. The effectiveness of the model is validated by analyzing the cognitive state of the brain before and after the intervention with electroencephalogram (EEG) neuroheadsets. Moreover, results obtained using the childhood autism rating scale (CARS) to measure the effectiveness of therapy also support the conclusions firmly. The statistical results with the p-value = 3.79E-07 < 0.05 and the F value = 23.93>3.28 show reliability and significance of the data. The results strongly indicate significant improvements in both modules, along with a notable improvement in multi-communication skills of the participating children.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-Robot Therapy for Improving Joint Attention and Imitation of ASD Children

et al. 2019

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“…Given the fact that most studies utilized ML to differentiate ASD from TD, future investigation could also be dedicated to the classification of ASD from other groups of individuals, such as between ASD and ADHD [40], [41]. In addition, ML could also be implemented in other aspects such as engagement evaluation [42]. With the increasing accumulation of data from genetic, neurological, psychological and kinematic fields, ML will be a promising tool that will eventually lead to the objective and automated diagnosis of ASD.…”

Section: Discussionmentioning

confidence: 99%

Applying Machine Learning to Identify Autism With Restricted Kinematic Features

Zhao¹,

Zhang²,

Li³

et al. 2019

IEEE Access

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Existing diagnosis of the autism spectrum disorder (ASD) heavily depends on the informant's evaluation of the patient's behavior, which is both time consuming and labor demanding. In order to develop a rapid diagnostic tool with high accuracy, machine learning (ML) approaches have been proposed to explore the feasibility of identifying ASD with a limited number of features extracted from behavioral evaluation, neuroimaging and kinematic data. Though restricted and repetitive behavior (RRB) is one of the cardinal symptoms of ASD, no study has been conducted to investigate whether restricted kinematic features (RKF) could be used to identify ASD. The present study aimed to address this question. Twenty children with high functioning autism and twenty-three children with typical development (TD) were recruited. They were instructed to perform a motor task that required the execution of the utmost variant movement. Entropy and 95% range of the movement amplitude, velocity and acceleration were computed as indices of RKF. Five ML classifiers were trained and tested including support vector machine (SVM), Linear Discriminant Analysis (LDA), Decision tree (DT), Random forest (RF), and K nearest neighbor (KNN). Results showed that the KNN algorithm (k = 1) yielded the highest classification accuracy with four kinematic features (accuracy: 88.37%, specificity: 91.3%, sensitivity: 85%, AUC: 0.8815). Our study demonstrated that RKF could help robustly identify ASD. It is inferred that the application of ML on genetic, neuroimaging, psychological and kinematic features might pose a considerable challenge to the current diagnostic criteria of ASD, and might potentially lead to an automated and objective diagnosis of ASD. INDEX TERMS Autism, entropy, kinematic feature, machine learning, restricted and repetitive behavior.

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“…The symbol "∘" indicates the dot product between matrices. Detailed content of engagement evaluation can be found in [23].…”

Section: Journal Of Roboticsmentioning

confidence: 99%

A Control Architecture of Robot-Assisted Intervention for Children with Autism Spectrum Disorders

Feng

Jia

Wei

2018

Journal of Robotics

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Robot-assisted intervention has been successfully applied to the education and training of children with autism spectrum disorders. However, it is necessary to increase the autonomy of the robot to reduce the burden on the human therapists. This paper focuses on proposing a robotic architecture to improve the autonomy of the robot in the course of the interaction between the robot and the child with autism. Following the model of perception-cognition-action, the architecture also incorporates some of the concepts of traditional autism intervention approach and the human cognitive model. The details of the robotic architecture are described in this paper, and in the end, a typical scenario is used to verify the proposed method.

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Engagement Evaluation for Autism Intervention by Robots Based on Dynamic Bayesian Network and Expert Elicitation

Cited by 24 publications

References 30 publications

An Adaptive Multi-Robot Therapy for Improving Joint Attention and Imitation of ASD Children

An Adaptive Multi-Robot Therapy for Improving Joint Attention and Imitation of ASD Children

Applying Machine Learning to Identify Autism With Restricted Kinematic Features

A Control Architecture of Robot-Assisted Intervention for Children with Autism Spectrum Disorders

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