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Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning
Mathematical Problems in Engineering ( IF 1.430 ) Pub Date : 2021-06-19 , DOI: 10.1155/2021/9955212
George A. Papakostas 1 , George K. Sidiropoulos 1 , Chris Lytridis 1 , Christos Bazinas 1 , Vassilis G. Kaburlasos 1 , Efi Kourampa 2 , Elpida Karageorgiou 2 , Petros Kechayas 3 , Maria T. Papadopoulou 4
Affiliation  

The task of child engagement estimation when interacting with a social robot during a special educational procedure is studied. A multimodal machine learning-based methodology for estimating the engagement of the children with learning difficulties, participating in appropriate designed educational scenarios, is proposed. For this purpose, visual and audio data are gathered during the child-robot interaction and processed towards deciding an engaged state of the child or not. Six single and three ensemble machine learning models are examined for their accuracy in providing confident decisions on in-house developed data. The conducted experiments revealed that, using multimodal data and the AdaBoost Decision Tree ensemble model, the children’s engagement can be estimated with 93.33% accuracy. Moreover, an important outcome of this study is the need for explicitly defining the different engagement meanings for each scenario. The results are very promising and put ahead of the research for closed-loop human centric special education activities using social robots.

中文翻译:

使用机器学习估算特殊教育中儿童与机器人互动的参与度

研究了在特殊教育过程中与社交机器人交互时儿童参与度估计的任务。提出了一种基于多模态机器学习的方法,用于估计有学习困难的儿童参与适当设计的教育场景的参与度。为此,在儿童机器人交互期间收集视觉和音频数据,并对其进行处理,以决定儿童是否处于参与状态。检查了六个单一和三个集成机器学习模型在为内部开发的数据提供自信决策方面的准确性。进行的实验表明,使用多模态数据和 AdaBoost 决策树集成模型,可以以 93.33% 的准确度估计儿童的参与度。而且,这项研究的一个重要结果是需要明确定义每个场景的不同参与含义。结果非常有希望,并领先于使用社交机器人进行以人为本的闭环特殊教育活动的研究。
更新日期:2021-06-19
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