magistrsko delo
Lovro Rojko (Author), Aleš Holobar (Mentor)

Abstract

V magistrskem delu smo proučevali možnost zaznavanja mišične utrujenosti s pomočjo senzorja Microsoft Kinect. Opisali smo zajem površinskih elektromiogramov (EMG) dvoglave in troglave nadlaktne mišice in kinetičnih meritev zgornjih okončin štirih zdravih merjencev in analizirali skupne karakteristike zajetih signalov. Iz kinetičnih meritev smo izračunali štiri veličine, in sicer pot, hitrost, pospešek in višino izvedene vaje. Časovne spremembe teh veličin smo statistično primerjali s spremembo amplitude signalov EMG, ki je znan pokazatelj mišične utrujenosti. Ugotovili smo, da se utrujenost mišice relativno dobro odraža v višini gibov, ostale kinetične metrike pa so bile za našo raziskavo manj informativne. Na podlagi teh rezultatov ocenjujemo, da je v primeru večkratnih ponovitev gibov zgornjih okončin možno zaznavati mišično utrujenost tudi samo iz kinetičnih meritev.

Keywords

vmesnik človek-stroj;elektromiogram;senzorji;Kinect;mišična utrujenost;kinetične meritve;RMS;površinski EMG;magistrske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher: [L. Rojko]
UDC: 004.5:612.744(043.2)
COBISS: 20702742 Link will open in a new window
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Other data

Secondary language: English
Secondary title: Muscle fatigue detection with surface electromyograms and microsoft kinect sensor
Secondary abstract: In this thesis, we examined the possibility of detecting muscle fatigue by using a Microsoft Kinect sensor. We simultaneously acquired surface electromyograms (EMG) of biceps brachii and triceps muscles and movements of upper extremity by Kinect sensor in four healthy subjects and analysed the common characteristics of the acquired signals. For each movement repetition, we calculated five different metrics from kinetic measurements, namely number of movement repetitions per time unit, distance, speed, acceleration and the maximal height of the arm. Temporal changes in these variables were statistically compared with the changes in the root mean square amplitude of the EMG signals, which is a well-known indicator of muscle fatigue. We found that muscle fatigue is relatively well reflected in the height of the arm, whereas the other tested kinetic metrics were less indicative for muscle fatigue. Based on these results we conclude that muscle fatigue can be detected from kinetic measurements of repeated upper limb movements.
Secondary keywords: human-computer interface;electromyogram;muscle fatigue detection;kinetic measurements;root mean square;
URN: URN:SI:UM:
Type (COBISS): Master's thesis/paper
Thesis comment: Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko, Računalništvo in informacijske tehnologije
Pages: XI, 55 str.
ID: 10842737