The Development of Attention Detection Model from Child Behavior for Robot-Assisted Autism Therapy

Wanglavan, Penpitcha; Jutharee, Wisanu; Maneewarn, Thavida; Kaewkamnerdpong, Boonserm

doi:10.23919/iccas47443.2019.8971509

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…The unobtrusiveness of face-tracking techniques enables establishing automatic systems to detect and classify attentional behaviors. In a previous study [55], an attention detection model was established based on the kid's behavior during engagement with the robot in HRI systems; the model was used in an adaptive interaction system where the robot detects the action depending on the child's attention. However, constructing such systems is challenging because of the complexity of attentional behavior in ASDs.…”

Section: Related Workmentioning

confidence: 99%

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Napoli¹,

Russo²,

Iocchi³

et al. 2022

OBM Neurobiol

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Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time.

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Section: Related Workmentioning

confidence: 99%

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Napoli¹,

Russo²,

Iocchi³

et al. 2022

OBM Neurobiol

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Applications of Robotics for Autism Spectrum Disorder: a Scoping Review

Santos,

Annunziata,

Geminiani

et al. 2023

Rev J Autism Dev Disord

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Robotic therapies are receiving growing interest in the autism field, especially for the improvement of social skills of children, enhancing traditional human interventions. In this work, we conduct a scoping review of the literature in robotics for autism, providing the largest review on this field from the last five years. Our work underlines the need to better characterize participants and to increase the sample size. It is also important to develop homogeneous training protocols to analyse and compare the results. Nevertheless, 7 out of the 10 Randomized control trials reported a significant impact of robotic therapy. Overall, robot autonomy, adaptability and personalization as well as more standardized outcome measures were pointed as the most critical issues to address in future research.

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Using Robot-Assisting Personalized Learning for Children with Autism: A Pilot Study of Robot's Actions

Paengkumhag,

Jutharee,

Limpornchitwilai

et al. 2023

2023 15th Biomedical Engineering International Conference (BMEiCON)

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The Development of Attention Detection Model from Child Behavior for Robot-Assisted Autism Therapy

Cited by 5 publications

References 5 publications

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Applications of Robotics for Autism Spectrum Disorder: a Scoping Review

Using Robot-Assisting Personalized Learning for Children with Autism: A Pilot Study of Robot's Actions

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