LSTM-Based Autoencoders in Online Handwriting Data Augmentation and Preprocessing

Simple item page
but.event.date23.04.2024cs
but.event.titleSTUDENT EEICT 2024cs
dc.contributor.authorGavenciak, Michal
dc.date.accessioned2024-07-09T07:38:39Z
dc.date.available2024-07-09T07:38:39Z
dc.date.issued2024cs
dc.description.abstractOn-line handwriting analysis is a research field that is among others used in assessment of handwriting difficulties (HD), which can be manifestations of degenerative brain diseases such as Parkinson’s disease in the elderly, or developmental dysgraphia in children. Using advanced modelling approaches or artificial intelligence is often difficult because of the limited data availability in both demographic cohorts. In this article, a data processing approach, using LSTM-based autoencoders, is described as a way of augmenting the database with semisynthetic data or preprocessing the data to improve the performance of feature-based classification. The proposed method has led to a 3 percentage point increase in classification accuracy when compared to baseline. While the improvement is marginal, it highlights another possible area of research to improve the efficacy of automated HD assessment.en
dc.formattextcs
dc.format.extent211-215cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings I of the 30st Conference STUDENT EEICT 2024: General papers. s. 211-215. ISBN 978-80-214-6231-1cs
dc.identifier.isbn978-80-214-6231-1
dc.identifier.issn2788-1334
dc.identifier.urihttps://hdl.handle.net/11012/249237
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 30st Conference STUDENT EEICT 2024: General papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectHandwriting difficultiesen
dc.subjectXGBoosten
dc.subjectLSTMen
dc.subjectautoencoderen
dc.titleLSTM-Based Autoencoders in Online Handwriting Data Augmentation and Preprocessingen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
211-eeict-2024.pdf
Size:
813.32 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Student EEICT 2024: General Papers