Surface topography systems enable the capture of
spinal dynamic movement. A visualization of possible unique
movement patterns appears to be difficult due to large intraclass and small inter-class variabilities. Therefore, we investigated
a visualization approach using Siamese neural networks (SNN)
and checked, if the identification of individuals is possible based
on dynamic spinal data. The presented visualization approach
seems promising in visualizing subjects in the presence of
intraindividual variability between different gait cycles as well
as day-to-day variability. Overall, the results indicate a possible
existence of a personal spinal ‘fingerprint’. The work forms the
basis for an objective comparison of subjects and the transfer of
the method to clinical use cases.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:79824 |
| Date | 07 July 2022 |
| Creators | Dindorf, Carlo, Konradi, Jürgen, Wolf, Claudia, Taetz, Betram, Bleser, Gabriele, Huthwelker, Janine, Werthmann, Friederike, Bartaguiz, Eva, Drees, Philipp, Betz, Ulrich, Fröhlich, Michael |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | urn:nbn:de:bsz:15-qucosa2-798165, qucosa:79816 |
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