This study aims to analyze the developmental characteristics of early childhood musical expressions from aviewpoint of movement elements, and to devise a method to evaluate the development regarding musical expressionin early childhood using machine learning. Previous studies regarding motion capture have shown analysis resultssuch as specific actions and responses to music (Burger et al, 2013). In this study, firstly, ANOVA was attempted onfull-body movements. The author quantitatively analyzed the motion capture data regarding 3-year-old, 4-year-old,and 5-year-old children in the nursery schools (n=84) and kindergartens (n=94) through a three-way non-repeatedANOVA. As a result, a statistically significant difference was observed in movement of body parts. Specifically, righthand movement such as moving distance and the moving average acceleration showed a significance of difference.Secondly, machine learning (decision trees, Sequential Minimum Optimization algorithm (SMO), Support VectorMachine (SVM) and neural network (multilayer perceptron)) was deployed to build classification models forevaluation of degree of musical development classified by educators with simultaneously recorded children’s videowith associated motion capture data. Among varieties of trained classification models, multilayer perceptron obtainedbest results of confusion matrix and showed fair classifying precision and usability to support educators to evaluatechildren’s achievement degree of musical development. As a result of the machine learning of multilayeredperceptron, the movement of the pelvis has a strong relationship with musical development degree. Its classificationaccuracy found consistent to affirm the availability to utilize the model to support educators to evaluate children’sattainment of musical expression.
Musical Development in Early Childhood
