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Interaction between children’s developmental degree of music and their musical expression continued to intrigue researchers. Currently, one noteworthy element will be to analyze such interaction from quantitative approach and to detect some predictive methodology to replicate such interaction statistically. In this study, the author extracted developmental characteristics of musical expressions in early childhood from viewpoints of elements of body movement, and applied a classification of machine learning based method on those feature quantities acquired from the participant children. Classification models were applied to the feature quantity for 3-year-old, 4-year-old, and 5-year-old in 2 nursery schools in 2016, 2 kindergartens in 2017 and a certified facility in 2018 utilizing 3D motion capture. In order to highlight developmental degree and to extract feature quantity, a three-way non-repeated ANOVA was applied and a statistically significant difference was observed in the movement data analyzed of the moving average of distance such as pelvis and right hand, the moving average of acceleration such as right hand, and the movement smoothness of right foot. The author classified the developmental degree of children’s musical expression by machine learning classifiers using the feature quantities of motion capture data after let classifiers train with categorical variables of developmental degree evaluated by the author with simultaneously recorded video. The author report here that the best classifier is Multilayer Perceptron Neural Network and the second best is Boosted Trees. The sensitivity result showed that the movement of the pelvis was strongly related to the musical development degree.

Predicting developmental degrees of music expression in early childhood by machine learning classifiers with 3D motion captured body movement data