A new framework has been developed to improve the assessment of Friedreich’s Ataxia (FRDA) in children, whose immature nervous systems complicate evaluations. Erratic movements and postural sway from normal development can obscure the effects of FRDA, affecting clinical rating scores. To address this, researchers propose a correction framework utilizing data from three specialized Inertial Measurement Units (IMUs): a cup-shaped device (AIM-C), a spoon-shaped device (AIM-S), and a pendant-shaped device (AIM-P). Each device employs tailored algorithms to isolate and eliminate developmental effects—using reinforcement learning for AIM-C, Bayesian optimization for AIM-S, and a multi-layer perceptron for AIM-P. These methods yield ataxia severity scores that focus solely on FRDA-related movement deficits, free from developmental confounding factors. The refined scores offer clinicians greater accuracy in measuring ataxia severity, enhancing the tracking of disease progression and treatment evaluation in pediatric FRDA patients. This study highlights the potential of advanced signal processing and machine learning to improve clinical assessments for children with this condition.