- Titel
- Visualization of interindividual differences in spinal dynamics in the presence of intraindividual variabilities
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-798241
- Konferenz
- LEVIA'22. Leipzig, 06.04.2022 - 07.04.2022
- Quellenangabe
- LEVIA'22
Herausgeber: Christina Gillmann
Herausgeber: Johanna Schmidt
Herausgeber: Stefan Jänicke
Herausgeber: Daniel Wiegreffe
Erscheinungsort: Leipzig
Erscheinungsjahr: 2022 - DOI
- https://doi.org/10.36730/2022.1.levia.6
- Abstract (EN)
- 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.
- Freie Schlagwörter (EN)
- Siamese Neural Networks, triplet loss, contrastive loss, surface topography, subject identification
- Klassifikation (DDC)
- 004
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa2-798241
- Veröffentlichungsdatum Qucosa
- 07.07.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
