Dominance in Visual Space of ASD Children Using Multi-Robot Joint Attention Integrated Distributed Imitation System

Mehmood, Faisal; Ayaz, Yasar; Ali, Sara; Amadeu, Rita De Cassia; Sadia, Haleema

doi:10.1109/access.2019.2951366

Cited by 24 publications

(10 citation statements)

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“…Furthermore, computer vision has been used to address autism therapy through social robots that automatically adapt their behaviours. For example, researchers 110 – 113 have presented systems that simultaneously include eye contact, joint attention, imitation and emotion recognition for an intervention protocol for ASD children. Egger et al 114 presented the first study showing the feasibility of computer vision techniques to automatically code behaviours in natural environments.…”

Section: Resultsmentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included [‘autis*’ AND (‘computer vision’ OR ‘behavio* imaging’ OR ‘behavio* analysis’ OR ‘affective computing’)]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.

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Section: Resultsmentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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“…The proposed therapy uses a NAO humanoid robot for ASD children. NAO is widely used for therapies in autism because of its anthropomorphic appearance [35], [36]. The robot is programmed to give three different kinds of stimuli to categorize the most effective stimulus for a particular level of autism.…”

Section: A Purpose Of Studymentioning

confidence: 99%

Comparing the Effectiveness of Different Reinforcement Stimuli in a Robotic Therapy for Children With ASD

et al. 2020

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Recent research has shown reliability in robotic therapies for improvement in core impairments of autism. To improve the efficiency of communication using robots, this study evaluates the effectiveness of three different stimuli in a robotic intervention for children with autism spectrum disorder. Three different reinforcement stimuli presented in least-to-most (LTM) order introduced in this therapy using NAO robot are: visual (color variation), auditory and motion cues. The therapy was tested on 12 ASD children, 4 out of 12 children fall under mild category whereas 8 fall under the minimal category of autism. The experimentation was conducted for 2 months. Total 8 experiments were conducted with 1 trial per week. Total 12 cues were given per trial, 4 cues corresponding to each category. In total 96 cues were given per subject, 32 cues from each category. The results indicate a general trend for linking a particular autism category with the most effective stimulus for that category. It can be concluded that visual cue (color variation) is the most effective reinforcement stimulus for children with minimal autism as 8 out of 8 i.e., 100% were more responsive to visual cues whereas for children with mild autism category, 3 out of 4 i.e., 75% are more receptive towards the motion stimulus. The parameters used for assessment were joint attention and the time eye contact is maintained. Single factor ANOVA was used for the statistical analysis of results with alpha is 0.05 and p-value 0.0342, F value is 3.7456 and F critical value is 3.2834. The test was performed on 96 (8x12) trails in total, therefore ensuring the significance and reliability of our results. INDEX TERMS Autism spectrum disorder (ASD), reinforcement stimulus, robotic therapy, joint attention.

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“…In special education, more specifically regarding Autism Spectrum Disorder (ASD) interventions, the ability of robots to improve the social and communication skills of children has been confirmed [3]. Numerous studies have demonstrated the use of social robots toward improving social skills such as joint attention [4][5][6]. Safety issues have also been considered [7].…”

Section: Introductionmentioning

confidence: 99%

Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques

Lytridis

Kaburlasos

Bazinas

et al. 2022

Sensors

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Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child’s behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child’s behavior.

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Dominance in Visual Space of ASD Children Using Multi-Robot Joint Attention Integrated Distributed Imitation System

Cited by 24 publications

References 50 publications

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Comparing the Effectiveness of Different Reinforcement Stimuli in a Robotic Therapy for Children With ASD

Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques

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