Quantifying Effects of Dataset Size, Data Variability, and Data Curvature on Modelling of Simulated Age-Related Motor Development Data

Performance modeling using robotic tasks can be used to identify motor control impairments. Motor development in children and youth occurs non-linearly, with higher accuracy and decreased variability in performance as they reach adulthood. However, past models have not accounted for these differences. The objective of this research was to create an algorithm that accounts for variability in sample size and age, modifying the curvature and variability of the data to test the accuracy and repeatability. While increased sample size improves the model, data collection is often limited by funding or population interest in participation. The simulations provide models of variability relative to sample size of the typically developing population from 5 to 18 years. The algorithm was evaluated with a sample of two-hundred and eighty-eight children. Using these models based on varying sample size, one can have greater confidence when identifying motor deficits from outlying data. Future researchers can use the model accuracy and repeatability information from this work to assess confidence in their own models based on the dataset size, data variability, and data curvature.