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Analysis of EMG Signal for Prosthetic hand based on Fuzzy Logic Technique
Ollé, Tamás ; Kolářová, Jana (oponent) ; Pathak, Pawan Kumar (vedoucí práce)
The human hand is an important limb essential for movement, grasping, perception , as well as being a vital part of the human body for sensation and communication. It is a classic example of how a complex mechanism can be implemented, capable of realizing very tedious and useful tasks using a very effective combination of mechanisms, sensing, actuation and control functions. Electrmyogram (EMG) was originally developed for the detection and further correction of muscular disorder. Further applications were soon evident, most importantly in epilepsy, and finally it became popular due to the introduction of prosthetics, specifically body powered prosthesis. EMG recording is used for studying the functional state of the muscle undr various motions when it undegoes stress and tension. The goal of this project is to develop electromyogram (EMG) classification methods that shall help in applications like real-time system. First Phase of this project was Data Acquisition. Real time data using PC based EMG Monitoring System (BIOPAC) was recorded and a complete data set of different subjects was obtained. This EMG data was converted from ASCII file to a readable form for MATLAB. Second Phase of this project was Feature Extraction. Five traditional parametric features, namely Integrated EMG (IEMG), Variance (VAR), Zero Crossings (ZC), Slope Sign Changes (SSC) and Waveform Length (WL) were extracted. Third phase of this project was Classification of EMG patterns using fuzzy logic techniques. The results were quite promising.
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Fuzzy Neural Networks for Pattern Classification
Ollé, Tamás ; Kontár, Stanislav (oponent) ; Vágnerová, Jitka (vedoucí práce)
This work describes the principle of operation of neurons and how they form artificial neural networks. The structure and the operation of neurons are thoroughly described and the most widely used algorithm for neuron training is shown as well as the basics of fuzzy logic including its advantages and disadvantages. This work fully describes the backpropagation algorithm and the adaptive neuro-fuzzy inference system. These techniques provide effective methods of neural network learning.
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Fuzzy Neural Networks for Pattern Classification
Ollé, Tamás ; Raida, Zbyněk (oponent) ; Vágnerová, Jitka (vedoucí práce)
This work describes the principle of operation of neurons and how they form artificial neural networks. The structure and the operation of neurons are thoroughly described and the most widely used algorithm for neuron training is shown as well as the basics of fuzzy logic including its advantages and disadvantages. This work fully describes the backpropagation algorithm and the adaptive neuro-fuzzy inference system. These techniques provide effective methods of neural network learning.
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Fuzzy Neural Networks for Pattern Classification
Ollé, Tamás ; Kontár, Stanislav (oponent) ; Vágnerová, Jitka (vedoucí práce)
This work describes the principle of operation of neurons and how they form artificial neural networks. The structure and the operation of neurons are thoroughly described and the most widely used algorithm for neuron training is shown as well as the basics of fuzzy logic including its advantages and disadvantages. This work fully describes the backpropagation algorithm and the adaptive neuro-fuzzy inference system. These techniques provide effective methods of neural network learning.
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Fuzzy Neural Networks for Pattern Classification
Ollé, Tamás ; Raida, Zbyněk (oponent) ; Vágnerová, Jitka (vedoucí práce)
This work describes the principle of operation of neurons and how they form artificial neural networks. The structure and the operation of neurons are thoroughly described and the most widely used algorithm for neuron training is shown as well as the basics of fuzzy logic including its advantages and disadvantages. This work fully describes the backpropagation algorithm and the adaptive neuro-fuzzy inference system. These techniques provide effective methods of neural network learning.
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Analysis of EMG Signal for Prosthetic hand based on Fuzzy Logic Technique
Ollé, Tamás ; Kolářová, Jana (oponent) ; Pathak, Pawan Kumar (vedoucí práce)
The human hand is an important limb essential for movement, grasping, perception , as well as being a vital part of the human body for sensation and communication. It is a classic example of how a complex mechanism can be implemented, capable of realizing very tedious and useful tasks using a very effective combination of mechanisms, sensing, actuation and control functions. Electrmyogram (EMG) was originally developed for the detection and further correction of muscular disorder. Further applications were soon evident, most importantly in epilepsy, and finally it became popular due to the introduction of prosthetics, specifically body powered prosthesis. EMG recording is used for studying the functional state of the muscle undr various motions when it undegoes stress and tension. The goal of this project is to develop electromyogram (EMG) classification methods that shall help in applications like real-time system. First Phase of this project was Data Acquisition. Real time data using PC based EMG Monitoring System (BIOPAC) was recorded and a complete data set of different subjects was obtained. This EMG data was converted from ASCII file to a readable form for MATLAB. Second Phase of this project was Feature Extraction. Five traditional parametric features, namely Integrated EMG (IEMG), Variance (VAR), Zero Crossings (ZC), Slope Sign Changes (SSC) and Waveform Length (WL) were extracted. Third phase of this project was Classification of EMG patterns using fuzzy logic techniques. The results were quite promising.
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