Automatic Balance Assessment Using Smartphone and AI

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dc.contributor.authorSganga, MagalĂ­en_US
dc.contributor.authorRozmiarek, Patrycjaen_US
dc.contributor.authorRavera, Emilianoen_US
dc.contributor.authorAkanyeti, Otaren_US
dc.contributor.authorPovina, Federico Villagraen_US
dc.contributor.editorVangorp, Peteren_US
dc.contributor.editorHunter, Daviden_US
dc.date.accessioned2023-09-12T05:45:07Z
dc.date.available2023-09-12T05:45:07Z
dc.date.issued2023
dc.description.abstractPostural 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.en_US
dc.description.sectionheadersVisual Computing
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20231206
dc.identifier.isbn978-3-03868-231-8
dc.identifier.pages137-140
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/cgvc.20231206
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20231206
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Artificial intelligence; Applied computing -> Life and medical sciences; >Human-centered computing -> Ubiquitous and mobile computing
dc.subjectComputing methodologies
dc.subjectArtificial intelligence
dc.subjectApplied computing
dc.subjectLife and medical sciences
dc.subjectHuman centered computing
dc.subjectUbiquitous and mobile computing
dc.titleAutomatic Balance Assessment Using Smartphone and AIen_US
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