Aging is a complex phenomenon that can be studied by analyzing age-related alterations in the execution of various tasks. This work deals with the processing of navigation data acquired from a bike simulator in two populations: young healthy subjects and older adults with loss of autonomy. Our goal is to analyze the influence of age in the ability to ride a bike on a virtual straight line with the perspective to be able to identify diseases in a geriatric population. For this purpose we process time series defined as the deviation from the center of the virtual path, in two straight lines: for each time series we quantify the irregularity – through sample entropy (SampEn) – of intrinsic mode functions obtained from complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The results show that our approach is able to reveal different learning and adaptation skills in the two populations. Our work could be useful to help the geriatricians in their diagnoses.