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Title:
Analiza zaznavanja aktivnosti prstov z zapestnico Myo
Authors:
ID
Galof, Nejc
(Author)
ID
Holobar, Aleš
(Mentor)
More about this mentor...
Files:
UN_Galof_Nejc_2016.pdf
(2,15 MB)
MD5: 4633E75673334902ABF98B2F15C91B18
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu analiziramo zaznavanje aktivnosti prstov z zapestnico Myo, s katero na področju podlahti z osmimi senzorji merimo površinske elektromiograme (EMG). Z meritvami dokažemo, da najlažje razpoznavamo posamične dvige iztegnjenih prstov, kjer dosežemo tudi do 95-odstotno natančnost. Pri gibih dveh prstov hkrati se natančnost zmanjša na 50 %. Aktivnosti palca na področju podlahti ne zaznavamo. Na kakovost razpoznave gibov prstov vplivajo omejenost gibljivosti prstov, prevodnost med kožo in senzorji EMG ter sama namestitev zapestnice Myo. Uporabnost zapestnice Myo za razpoznavo gibov prstov prikažemo z aplikacijo, ki omogoča igranje virtualnega klavirja.
Keywords:
zapestnica Myo
,
razpoznava gibov prstov
,
površinski elektromiogrami
,
virtualni klavir
Place of publishing:
[Maribor
Publisher:
N. Galof
Year of publishing:
2016
PID:
20.500.12556/DKUM-61511
UDC:
004.81:[602.1:681.5(043.2)
COBISS.SI-ID:
19884054
NUK URN:
URN:SI:UM:DK:CHUWHOA7
Publication date in DKUM:
06.09.2016
Views:
1776
Downloads:
250
Metadata:
Categories:
KTFMB - FERI
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:
GALOF, Nejc, 2016,
Analiza zaznavanja aktivnosti prstov z zapestnico Myo
[online]. Bachelor’s thesis. Maribor : N. Galof. [Accessed 1 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=61511
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Secondary language
Language:
English
Title:
Analysis of finger activity detection with Myo armband
Abstract:
We analyse the feasibility of finger activity detection by the Myo armband. The latter is placed on the forearm of the healthy male subject and measures eight surface electromyogram (EMG) channels from different finger extensors and flexors, except the thumb. We systematically analyse different finger movements. Our algorithm detects the extensions of fingers with accuracy of 95 %, whereas detection of other finger movements proves to be less accurate. Movements of two fingers decrease the accuracy of movement detection to 50 %. Quality of finger detection depends on the flexibility of subject’s fingers, electrical resistance between the skin and the EMG sensors and placement of the Myo armband. We demonstrate the usage of finger activity detection with a virtual piano application.
Keywords:
Myo armband
,
finger movement detection
,
surface electromyograms
,
virtual piano
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