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Title:
Analiza večkanalnih elektromiogramov mišic podlahti med igranjem bas kitare : diplomsko delo
Authors:
ID
Klančar, Luka
(
Author
)
ID
Holobar, Aleš
(
Mentor
)
More about this mentor...
Files:
UN_Klancar_Luka_2020.pdf
(24,89 MB)
MD5: 587D1E487A8F4611F7B3EEFE71D3E406
PID:
20.500.12556/dkum/df095d12-eb47-4537-81f9-ac64e2185bac
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V zaključnem delu smo preučili primernost večkanalnih površinskih elektromiogramov (EMG) za analizo električne aktivnosti upogibalk in iztegovalk štirih prstov leve roke med igranjem bas kitare. Zajeli smo večkanalne elektromiografske signale dveh različnih gibov vsakega prsta. Signale smo zajemali z dvema poljema elektrod in jih v programskih orodjih Matlab in PyCharm klasificirali časovno, prostorsko in s pomočjo strojnega učenja. Izdelali smo grafični vmesnik z drsnikom, ki prikazuje časovno sosledje aktivacij motoričnih enot prek polja elektrod. Klasifikacijo strojnega učenja smo opravili z metodo podpornih vektorjev (SVM) in metodo naključnih gozdov (RF). Dotike različnih prstov smo prepoznali s povprečno natančnostjo 90% ob uporabi metode SVM in 96,5% ob uporabi metode RF.
Keywords:
večkanalni elektromiogram
,
mišice podlahti
,
bas kitara
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[L. Klančar]
Year of publishing:
2020
Number of pages:
XXI, 61 str.
PID:
20.500.12556/DKUM-77407
UDC:
004.93:612.743(043.2)
COBISS.SI-ID:
38833667
NUK URN:
URN:SI:UM:DK:W48FIODR
Publication date in DKUM:
03.11.2020
Views:
868
Downloads:
162
Metadata:
Categories:
KTFMB - FERI
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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:
29.08.2020
Secondary language
Language:
English
Title:
Analysis of high-density electromyograms of the forearm muscles during bass guitar playing
Abstract:
In this diploma thesis we studied the adequacy of multi-channel surface electromyograms (EMG) for the analysis of multichannel electromyographic (EMG) signals produced by the flexors and extensors of four fingers of the left arm during bass guitar playing. We studied the signals of two different motions produced by each finger. We recorded EMG signals by two arrays of electrodes and analysed them with the help of Matlab and PyCharm program tools. We classified the recorded signals chronologically, spatially and with the help of machine learning methods. We implemented a simple graphic interface, that allows for the viewing of averaged EMG signals chronologically and on each electrode. Machine learning classification yielded an average accuracy of 90% and 96.5% for support vector machines and random forest methods, respectively.
Keywords:
high-density electromyogram
,
forearm muscles
,
bass guitar
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