Sganga, MagalíRozmiarek, PatrycjaRavera, EmilianoAkanyeti, OtarPovina, Federico VillagraVangorp, PeterHunter, David2023-09-122023-09-122023978-3-03868-231-8https://doi.org/10.2312/cgvc.20231206https://diglib.eg.org:443/handle/10.2312/cgvc20231206Postural control assessment is essential for understanding human biomechanics in both static and dynamic situations. The relationship between the center of mass (CoM), center of pressure (CoP), and the base of support (BoS) determines whether a person is capable to maintain the balance. Inertial motion units (IMUs) are portable and cost-effective devices capable of measuring acceleration and angular velocity. The integration of IMUs into smartphones provides an accessible means of evaluating postural control in the general population without the need for expensive and time-consuming laboratory setups. A convolutional neural network (CNN) architecture will be employed to predict the difference between the CoM and CoP behavior during different tasks with data from an optoelectronic motion capture system combined with instrumented treadmill. This study aims to establish the foundation for developing an application that assesses postural control and balance in both healthy and pathological populations.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> Artificial intelligence; Applied computing -> Life and medical sciences; >Human-centered computing -> Ubiquitous and mobile computingComputing methodologiesArtificial intelligenceApplied computingLife and medical sciencesHuman centered computingUbiquitous and mobile computing10.2312/cgvc.20231206137-1404 pages