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Title:Sistem za beleženje zgodovine ročnega urejanja rezultatov dekompozicije večkanalnih površinskih elektromiogramov : diplomsko delo
Authors:ID Murks, Nina (Author)
ID Holobar, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Murks_Nina_2022.pdf (5,33 MB)
MD5: 3B8F3B76A1C191CE7A97E2EA573EFDF9
PID: 20.500.12556/dkum/948e05a1-286f-41c7-a123-5e3e2bed5ac6
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Namen magisterska dela je izdelati sistem za shranjevanje zgodovine ročnega urejanja rezultatov dekompozicije večkanalnih površinskih elektromiogramov ter prikaz njegove uporabe. Področje obsega predstavljene strategije urejanja rezultatov dekompozicije, opis delovanja ter implementacije sistema za shranjevanje zgodovine in njegovo uporabo. Prva predstavljena uporaba sistema za beleženje zgodovine je izračun in prikaz statistike urejanja, s pomočjo katere je možno vrednotiti strategije urejanja. Drugi primer uporabe sistema za beleženje zgodovine je preprost primer delne avtomatizacije urejanja, pri čemer sta uporabljena dva različna modela nevronskih mrež. Prvi model vsebuje konvolucijske sloje, drugi pa sloje LSTM. Preučili smo uspešnost obeh modelov ter prikazali njune rezultate. Model s konvolucijskimi sloji je dosegel 71-% preciznost ob 83-% priklicu napovedi urejanja, model s sloji LSTM pa 100-% preciznost ob 75-% priklicu.
Keywords:beleženje zgodovine, rezultati dekompozicije, statistika urejanja, delna avtomatizacija urejanja
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[N. Murks]
Year of publishing:2022
Number of pages:1 spletni vir (1 datoteka PDF (X, 69 f.))
PID:20.500.12556/DKUM-81801 New window
UDC:004.9:621.391(043.2)
COBISS.SI-ID:113787395 New window
Publication date in DKUM:14.06.2022
Views:951
Downloads:150
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
MURKS, Nina, 2022, Sistem za beleženje zgodovine ročnega urejanja rezultatov dekompozicije večkanalnih površinskih elektromiogramov : diplomsko delo [online]. Master’s thesis. Maribor : N. Murks. [Accessed 18 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=81801
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:01.06.2022

Secondary language

Language:English
Title:Logging of the manual editing of high-density surface electromyogram decomposition results
Abstract:The master's thesis aims to develop a logging system for the manual editing of high-density surface decomposition results and to demonstrate its use. The scope of the work includes the proposed strategies for editing the decomposition results, a description of how the logging system works, its implementation, and its use. In the first demonstration, the logging system is used to calculate and present different statistics of editing, which can be used to estimate editing strategies. A simple example of partial automation using two different neural network models is another example of using a logging system. The first model uses convolutional layers, while the second uses LSTM layers. Both models were evaluated, and their results were summarized. In the forecasting of editing actions, the model with convolutional layers demonstrated 71% precision with 83% recall, while the model with LSTM layers achieved 100% precision and 75% recall.
Keywords:Logging system, decomposition results, editing statistics, partial editing automation


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