Comparing Symbolic and Connectionist Algorithms for Correlating the Age of Healthy Children with Sigma-Lognormal Neuromuscular Parameters

It is important to accurately evaluate the motor control maturity to help physicians diagnose delayed or abnormal motor development in children. Traditionally, it has been challenging to design assessment methods that are practical and accurate at the same time. This study aims to develop an effective algorithm to predict motor control maturity based on the Kinematic Theory of rapid human movements. We used handwritten pen strokes made on an electronic tablet by 513 children (5.5 to 13 years of age). We considered two types of movements: a single stroke and a triangle drawing test. For the analysis, Sigma-Lognormal parameters were extracted from recordings and used in predictive models. We compared multiple models, including linear regression, deep learning, K-nearest neighbors regression, random forest, gradient boosting regression, and support vector regression. These models performed well considering the within-children variability in handwritten strokes and the between-children variability in motor control maturity. The best score was obtained using the neural network model: coefficient of determination (R2): 0.548; mean absolute error (MAE): 0.937. We found simple stroke parameters alone to be sub-optimal; the results were better when using parameters from the triangle test. In conclusion, our study demonstrates that the Sigma-Lognormal model offers new possibilities for estimating the motor control maturity. Our method is fast and comfortable for the children, as it only requires performing handwriting strokes on an electronic tablet. This simple and user-friendly test is expected to be more convenient for doctors in a clinical context