Machine Learning Builds Embedded Interaction Model to Guide Knocking Behavior in 3-6 Year Olds

Abstract: Machine learning, as an emerging technology, is gradually starting to be applied in the field of children’s education (Nan, 2020). This article mainly investigates how to utilize machine learning technology to assist parents in guiding children aged 3-6 to develop good behavioral habits and manners. However, existing design studies aimed at children’s behavioral habits lack relevant academic experimental cases and also have certain technical limitations. Therefore, in this article, through user interviews, observational methods, and the creation of the FBM behavior model, the results of the interviews with the parents of seven children, aged between three and six years, were utilized as a basis for further observational analysis of the daily behavior of one child aged 3. In order to determine the direction of research on children’s formation of good knocking habits, two experimental studies were carried out while studying the behavioral model of children aged 3-6. The first experiment, based on the research results obtained through interviews and observations, children’s footsteps were identified as the training object of the machine learning MFE model to complete the model data construction. The second experiment involved building machine learning training models to configure hardware-device interaction models. The model was then deployed to the surveyed families for further validation and tracking of the children’s behavior. Finally, it was further confirmed that children’s behavior can be subtly changed with guidance, thereby fostering the habit of knocking on the door. Simultaneously, the research findings also indicate that leveraging machine learning to assist in guiding the formation of good behavioral habits among children aged 3-6 is a feasible and deeply valuable research direction.

Keywords: Children’s Behavioral Guidance, Behavioral Habits, Fogg Behavior Model (FBM), Machine Learning, Footstep Recognition

DOI: 10.54941/ahfe1004802

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