Feraldo, Feraldo and Budiarto, Eka and Rusyadi, Rusman (2018) Analysis And Implementation Of Hand Gesture Classification From Surface Electromyography (SEMG) Signal Using Myo Armband For Bionic Hand. Bachelor thesis, Swiss German University.

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Abstract

The goal of this thesis is to detect Surface Electromyography (SEMG) signal from a person’s arm using Myo Armband and classify his / her performed finger gestures based on the corresponding signal. The system was constructed in Robotic Operating System (ROS) environment, because it helps to make the development of the software more straightforward. Artificial Neural Network (based on the machine learning approach) and Principal Component Analysis (based on the feature extraction approach) with and without Fast Fourier Transform (FFT) were selected as the methods utilized in this research. Analysis results show that ANN has achieved 62.14% gesture classifying accuracy, while PCA without FFT has achieved 30.43% and PCA without FFT has achieved 48.15% accuracy. The three classifiers are tested using SEMG data from a set of six recorded custom gestures. With the gesture classification software constructed, it is then further developed to implement the controller of the Ada Hand (product of OpenBionics) fingers using Raspberry Pi 3 Mini PC with ROS and Arduino.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Bionic Hand; Surface Electromyography; Artificial Neural Network; Principal Component Analysis; Gesture Classification
Subjects:	R Medicine > RC Internal medicine > RC77.5 Electromyography T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions:	Faculty of Engineering and Information Technology > Department of Mechatronics Engineering
Depositing User:	Astuti Kusumaningrum
Date Deposited:	26 Sep 2020 14:23
Last Modified:	26 Sep 2020 14:23
URI:	http://repository.sgu.ac.id/id/eprint/837

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